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Massive amounts of real-time streaming network data are generated quickly because of the exponential growth of applications. Analyzing patterns in generated flow traffic streaming offers benefits in reducing traffic congestion, enhancing network management, and improving the quality of service management. Processing massive volumes of generated traffic poses more challenges when data traffic encryption is raised. Classifying encrypted network traffic in real-time with deep learning networks has received attention because of their excellent performance. The substantial volume of incoming packets, characterized by high speed and wide variety, puts real-time traffic classification within the domain of big data problems. Classifying traffic with high speed and accuracy is a significant challenge in the era of big data. The real-time nature of traffic intensifies deep learning networks, necessitating a considerable number of parameters, layers, and resources for optimal network training. Until now, various datasets have been employed to evaluate the effectiveness of previous methods for classifying encrypted traffic. The primary objective has been to enhance accuracy, precision, and F1-measure. Presently, encrypted traffic classification performance depends on pre-existing datasets. The learning and testing phases are done offline, and more research is needed to investigate the feasibility of these methods in real-world scenarios. This paper examines the possibility of a tradeoff between evaluating the model's effectiveness, execution time, and utilization of processing resources when processing stream-based input data for traffic classification. We aim to explore the feasibility of establishing a tradeoff between these factors and determining optimal parameter settings. This paper used the ISCX VPN-Non VPN 2016 public dataset to evaluate the proposed method. All packets from the dataset were streamed continuously through Apache Kafka to the classification framework. Numerous experiments have been designed to demonstrate the efficacy of the proposed method. The experimental results show that the proposed method outperforms the baseline methods by 11% in the F1-measure when the number of workers is two and by 25% when the number of workers is equal to 32. [ABSTRACT FROM AUTHOR]

A web search, an online video, a connected Nest device, and hundreds of cloud services all give us a response in a fraction of a second. But what really happens when we click search or send a request. The request travels over the public internet and into fiber network. Millions of requests or packets of data travel through miles of cable over land and under sea, converging at one of the many data centers that operate all over the world. Data Center (DC) is the core site of data operation, storage and forwarding, which is also an important part of cloud platform. A large number of commercial switches and servers are usually deployed at the DC. The DC is a complex set of facilities. Data center networks (DCN) is a network applied in the DC, because the traffic in DC presents the typical characteristics of centralized exchange data and increased traffic, which puts forward further requirements for the DCN. DCN connects a large-scale server cluster and is a bridge for data transmission and storage. With the expansion of DC and the increasing number of service types, communication within data centers becomes more frequent. On the other hand, traffic between data centers has also increased dramatically. Considering the multi-layered transmission mode and traffic characteristics of DCN, this paper proposes a software defined networking (SDN) -based multi-layered traffic scheduling scheme for DCN, which is mainly focused on hop count, criticality and cost. Moreover, based on SDN architecture and Deep Q-Network of Reinforcement Learning (RL), the intelligent multi-layered traffic scheduling scheme is proposed to obtain the current optimal global routing strategy according to the real-time traffic demand in the network. The simulation results show that the proposed scheme outperforms benchmarks in terms of average throughput, normalized total throughput, link bandwidth utilization, average round-trip time and network traffic bandwidth loss rate. [ABSTRACT FROM AUTHOR]

The classification of network traffic has become increasingly crucial due to the rapid growth in the number of internet users. Conventional approaches, such as identifying traffic based on port numbers and payload inspection are becoming ineffective due to the dynamic and encrypted nature of modern network traffic. A number of researchers have implemented Software Defined Networking (SDN) based traffic classification using Machine Learning (ML) and Deep Learning (DL) models. However, the studies had various limitations such as encrypted traffic detection, payload inspection, poor detection accuracy, and challenges with testing models both in offline and real-time traffic modes. ML models together with SDN are adopted nowadays to enhance classification performance. In this paper, both supervised (Logistic Regression, Decision Tree, Random Forest, AdaBoost, and Support Vector Machine) and unsupervised (K-means clustering) ML models were used to classify Domain Name System (DNS), Telnet, Ping, and Voice traffic flows simulated using the Distributed Internet Traffic Generator (D-ITG) tool. The use of this tool effectively manages and classifies traffic types based on their application. The study discussed the dataset used, model selection, implementation of the model, and implementation techniques (such as pre-processing, feature extraction, ML algorithm, and model evaluation metrics). The proposed model in SDN was implemented in Mininet for designing the network architecture and generating network traffic. Anaconda Python environment was utilized for traffic classification using various ML techniques. Among the models tested, the Decision Tree supervised learning achieved the highest accuracy of 99.81%, outperforming other supervised and unsupervised learning algorithms. These results indicate that the integration of ML with SDN provides an efficient classification method for identifying and accurately classifying both offline and real-time network traffic, enhanced quality of service (QoS), detection of encrypted packets, deep packet inspection and management. [ABSTRACT FROM AUTHOR]

Federated Learning is an effective solution to address the issues of data isolation and privacy leakage in machine learning. However, ensuring the security of network devices and architectures deploying federated learning remains a challenge due to network attacks. This paper proposes an attention-based Graph Neural Network for detecting cross-level and cross-department network attacks. This method enables collaborative model training while protecting data privacy on distributed devices. By organizing network traffic information in chronological order and constructing a graph structure based on log density, enhances the accuracy of network attack detection. The introduction of an attention mechanism and the construction of a Federated Graph Attention Network (FedGAT) model are used to evaluate the interactivity between nodes in the graph, thereby improving the precision of internal network interactions. Experimental results demonstrate that our method achieves comparable accuracy and robustness to traditional detection methods while prioritizing privacy protection and data security. [ABSTRACT FROM AUTHOR]

This survey paper provides a comprehensive overview of recent research and development in network security that uses graphs and graph-based data representation and analytics. The paper focuses on the graph-based representation of network traffic records and the application of graph-based analytics in intrusion detection and botnet detection. The paper aims to answer several questions related to graph-based approaches in network security, including the types of graphs used to represent network security data, the approaches used to analyze such graphs, the metrics used for detection and monitoring, and the reproducibility of existing works. The paper presents a survey of graph models used to represent, store, and visualize network security data, a survey of the algorithms and approaches used to analyze such data, and an enumeration of the most important graph features used for network security analytics for monitoring and botnet detection. The paper also discusses the challenges and limitations of using graph-based approaches in network security and identifies potential future research directions. Overall, this survey paper provides a valuable resource for researchers and practitioners in the field of network security who are interested in using graph-based approaches for analyzing and detecting malicious activities in networks. [ABSTRACT FROM AUTHOR]

The rapidly growing number of Internet of Things (IoT) devices has led to a rise in data transfers, which has raised security concerns. Due to the devices' limited processing capabilities and vulnerability to many cyber attacks, securing IoT communications is challenging. Security threats, especially Distributed Denial of Service (DDoS) attacks, take a toll on the network in the form of increased communication overhead. Hence, a centralized unit is required to detect DDoS attacks in IoT networks at the earliest. Software-Defined Networking (SDN) promises a potential solution for better network traffic management and data flow. This paper presents a machine learning-based ensemble model for the detection of DDoS attacks in IoT networks using SDN. The proposed model employs a multi-step approach utilizing various Machine Learning (ML) algorithms. The proposed Ensemble Model (EM) combines Logistic Regression (LR), k-Nearest Neighbors (KNN), Gradient Boosting (GB), Extra-tree (ET), AdaBoost, and XGBoost, with XGBoost as the final estimator classifier. Various metrics, including sensitivity, specificity, precision, accuracy, and others, derived from the confusion matrix, evaluate the proposed model's performance. The EM demonstrates superior performance during comparative analysis with state-of-the-art schemes, with a classification accuracy of 99.8%. Furthermore, the paper evaluates the model based on Receiver Operator Characteristic (ROC) curves, showing its superiority in True Positive Rates (TPR) compared to False Positive Rates (FPR). The AUC analysis supports the EM's effectiveness. Cross-validation results further validate the model's robustness, with a mean accuracy of 97.92%. [ABSTRACT FROM AUTHOR]

In the evolving landscape of Open Radio Access Networks (Open RAN), the dynamic and unpredictable nature of network conditions presents significant challenges for traditional spectrum allocation strategies. This paper introduces an innovative framework that leverages Federated Learning (FL) to refine and enhance spectrum allocation within Open RAN. Utilizing the decentralized architecture of FL, our model introduces a system that is not only more adaptive to real-time changes but also offers enhanced robustness for spectrum management. We delve into the advantages of this approach, such as significant improvements in data traffic management, latency reduction, and overall network capacity enhancement. Additionally, we address potential implementation challenges, providing strategic countermeasures to ensure the successful deployment of our FL-based framework. Through this exploration, our paper underscores the transformative potential of integrating FL with Open RAN, marking a significant step forward in the application of AI technologies for optimizing wireless communication networks. This contribution opens new avenues for research in AI-driven spectrum allocation, setting a foundation for future empirical validations and the development of more efficient, intelligent telecommunication infrastructures. [ABSTRACT FROM AUTHOR]

The traditional routing method makes use of limited information on the network links to make routing decisions, which makes it difficult to adapt to the dynamic and complex network and adjust the router's forward strategy. To address these issues, this paper proposes an intelligent routing method based on the Software Defined Network (SDN), Dueling DQN (a Deep Reinforcement Learning algorithm) and network traffic state prediction. First, the global network awareness information is obtained with the SDN network measurement mechanism, which is converted into a traffic matrix consisting of multiple network link status information such as bandwidth and delay, etc. Then, the optimal forwarding route under the current network state is generated by predicting the network traffic matrix and the Dueling DQN. The experimental results show that: (1) compared with the traditional Dijkstra and OSPF routing methods, the proposed method significantly improves the network throughput and effectively reduces the network delay and packet loss rate; (2) comparing with the reinforcement learning algorithms DDPG and PPO, the proposed approach achieves a faster convergence state, which improves the efficiency of network routing. [ABSTRACT FROM AUTHOR]

Using deep learning models to deal with the classification tasks in network traffic offers a new approach to address the imbalanced Internet of Things malicious traffic classification problems. However, the employment difficulty of these models may be immense due to their high resource consumption and inadequate interpretability. Fortunately, the effectiveness of sampling methods based on the statistical principles in imbalance data distribution indicates the path. In this paper, we address these challenges by proposing a hybrid sampling method, termed HSS, which integrates undersampling and oversampling techniques. Our approach not only mitigates the imbalance in malicious traffic but also fine-tunes the sampling threshold to optimize performance, as substantiated through validation tests. Employed across three distinct classification tasks, this method furnishes simplified yet representative samples, enhancing the baseline models' classification capabilities by a minimum of 6.02% and a maximum of 182.66%. Moreover, it notably reduces resource consumption, with sample numbers diminishing to a ratio of at least 83.53%. This investigation serves as a foundation, demonstrating the efficacy of HSS in bolstering security measures in IoT networks, potentially guiding the development of more adept and resource-efficient solutions. [ABSTRACT FROM AUTHOR]

One of the key operations in the Public Key Infrastructure (PKI) is certificate revocation management. In essence, revocation is a process to check the reliability of the certificate. A certificate is effectively deemed invalid when it is revoked, a procedure commonly referred to as PKI certificate revocation. The revoked certificates are kept on the certificate revocation list (CRL). The latest revocation list is always required by the client to validate the certificate. As a result, flooding occurs when all clients on the network request the new list at the new revoked list's publish time. There is also a rapid increase in network traffic, eventually increasing the CRL size and also utilizing high bandwidth. Flooding for updates and the rise in CRL size can cause a single point of failure. Hence, efficient revocation mechanisms are needed to manage the network. This paper proposes efficient revocation management by partitioning the CRL to manage its size and building a filter data structure (FDS) to verify the status of the certificates. Instead of parsing the entire list to check the revocation status, the novel mechanism allows us to check whether the revoked certificate is in the bloom filter. As a result, the search time is reduced to O(1) from O(n). Moreover, this paper addresses the scalability issue through partitioning and also minimizes the search retrieval time. The proposed scheme is evaluated against various schemes. The performance of the proposed scheme shows it outperforms existing schemes concerning latency, network traffic overload reduction, and scalability. [ABSTRACT FROM AUTHOR]

Network flow watermark technology is a traffic marking technique that embeds watermark information into the characteristics of network flows to mark and trace attack flows generated by network attackers. However, with the development of network attack techniques, the time and number of packets required for network attacks have decreased. Existing network flow watermark technologies fail to balance watermark robustness and efficiency, resulting in poor practicality. To address this issue, this paper proposes an efficient hexadecimal network flow watermark method. The method introduces an efficient interval watermark algorithm and utilizes an interval synchronization algorithm to self-learn watermark parameters, thereby improving the encoding efficiency of the watermark. The design of watermark start and end markers ensures the practicality of network watermarks, enabling traceability and source attribution of attack flows in real network environments. The proposed method is experimentally tested using real network traffic, and the results demonstrate that even in the presence of a network jitter, the watermark detection success rate of this scheme remains above 95%. Compared to other network flow watermark schemes, the hexadecimal network flow watermark proposed in this paper achieves a 50% improvement in encoding and decoding efficiency while ensuring robustness. It also exhibits excellent resistance to network jitter, packet loss, and false packet insertion. [ABSTRACT FROM AUTHOR]

The vast volume of redundant and irrelevant network traffic data poses significant hurdles for intrusion detection. Effective feature selection is crucial for eliminating irrelevant information. Presently, most filtering and embedded methods rely on fixed thresholds or ratios, necessitating prior knowledge. Conversely, wrapper methods are computationally intensive, and individual feature selection methods may introduce biases in evaluation. To address these challenges, this study introduces Adaptive Neighborhood based Feature Selection (AN-SFS), a dynamic feature selection approach that adapts to local statistical properties of the data. Unlike traditional methods, AN-SFS adjusts its threshold based on the characteristics of the current feature subset and incorporates statistical measures of neighboring features, capturing subtle relationships and dependencies. This adaptability enables AN-SFS to achieve robust and effective feature selection outcomes. Using NSL-KDD and UNSW-NB15 datasets, our model demonstrates superiority over conventional ML classifiers in detection rate, precision, and recall, achieving outstanding accuracy rates of 99.3% on NSL-KDD and 97.5% on UNSW-NB15, significantly outperforming contemporary methods. To further demonstrate the effectiveness of our feature selection approach, we conducted a series of comparisons with other statistical methods, including ANOVA, Pearson correlation, and the chi-square test. In each comparison, our approach consistently outperformed these alternatives, underscoring its superior efficacy. Highlights: This paper introduces a new method for choosing important features from a dataset, called Adaptive Neighborhood-based Statistical Feature Selection (AN-SFS). This technique picks the most relevant features by using statistical data and information from neighbouring data points. We also perform feature selection specifically for different types of attacks by dividing the dataset into subsets that represent various attack categories. We then identify and choose the most relevant features for each type of attack based on their importance. We test our method using the NSL-KDD and UNSW-NB15 datasets. Our experiments demonstrate that our approach effectively distinguishes between normal IoT traffic and different attack scenarios. Notably, it achieves these results with low model complexity and a compact size. [ABSTRACT FROM AUTHOR]

The evolving behavior of the attacks may affect the decision boundaries of the trained machine learning models. The issue has not been well investigated, especially with hypervisor-based security solutions where virtual machine (VM)'s network artifacts are introspected and analyzed. In this paper, we proposed a sustainable and explainable flow-filtering-based concept drift-driven network intrusion detection approach, called 'SFC-NIDS' which introspects network activities by analyzing VM traffic profile. The VM traffic is captured and pre-processed at the hypervisor to extract important network artifacts. The redundant and trivial network flows have been filtered using the proposed gradient descent-based flow filtering mechanism and validated using explainability. SFC-NIDS employs auto-encoders to reconstruct the traffic features to capture additional patterns. Afterward, the 1D-convolution neural network has been employed to learn and detect malicious attack flows. The model's sustainability is ensured by integrating the drift detection mechanism with the decision model to retrain it with evolving attack patterns. The approach has been validated with virtual network traffic artifacts collected at the hypervisor and provides 98.9% accuracy, 99.03%, and F1-Score. In addition, the approach has also been validated using the KDD99 dataset, showcasing an accuracy of 99.97% and an F1-Score of 99.98%. [ABSTRACT FROM AUTHOR]

Recent technological advancements have significantly expanded both networks and data, thereby introducing new forms of attacks that pose considerable challenges to intrusion detection and network security. With intruders deploying increasingly diverse attack vectors, the need for robust Intrusion Detection Systems (IDSes) has become paramount. IDS serves as a crucial tool for monitoring network traffic to uphold the integrity, confidentiality, and availability of systems. Despite the integration of Machine Learning (ML) and Deep Learning (DL) algorithms into IDS frameworks, achieving higher accuracy levels while minimizing false alarms remains a challenging task, especially when handling large datasets. In response to this challenge, researchers have turned to bio-inspired algorithms as potential solutions to enhance IDS models. This paper undertakes a comprehensive literature review focusing on augmenting the security of Internet of Things (IoT) networks by integrating bio-inspired methodologies with ML and DL techniques. Among 145 published articles, 25 relevant studies were selected to address the defined research objectives. The findings underscore the efficacy of combining bio-inspired techniques with ML and DL approaches in enhancing IDS performance, highlighting their potential to bolster IoT network security. Furthermore, the review incorporates a comparative analysis of the selected articles, considering various factors, and outlines ongoing challenges and future directions in integrating bio-inspired techniques with ML and DL algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a sophisticated approach to network security, with a primary emphasis on utilizing deep learning for intrusion detection. In real-world scenarios, the high dimensionality of training data poses challenges for simple deep learning models and can lead to vanishing gradient issues with complex neural networks. Additionally, uploading network traffic data to a central server for training raises privacy concerns. To tackle these issues, the paper introduces a Residual Network (ResNet)-based deep learning model trained using a federated learning approach. The ResNet effectively tackles the vanishing gradient problem, while federated learning enables multiple Internet Service Providers (ISPs) or clients to engage in joint training without sharing their data with third parties. This approach enhances accuracy through collaborative learning while maintaining privacy. Experimental results on the X-IIoTID dataset indicate that the proposed model outperforms conventional deep learning and machine learning methods in terms of accuracy and other metrics used for evaluation. Specifically, the proposed methodology achieved 99.43% accuracy in a centralized environment and 99.16% accuracy in a federated environment. [ABSTRACT FROM AUTHOR]

With the exponential increase in the urban population, urban transportation systems are confronted with numerous challenges. Traffic congestion is common, traffic accidents happen frequently, and traffic environments are deteriorating. To alleviate these issues and improve the efficiency of urban transportation, accurate traffic forecasting is crucial. In this study, we aim to provide a comprehensive overview of the overall architecture of traffic forecasting, covering aspects such as traffic data analysis, traffic data modeling, and traffic forecasting applications. We begin by introducing existing traffic forecasting surveys and preliminaries. Next, we delve into traffic data analysis from traffic data collection, traffic data formats, and traffic data characteristics. Additionally, we summarize traffic data modeling from spatial representation, temporal representation, and spatio-temporal representation. Furthermore, we discuss the application of traffic forecasting, including traffic flow forecasting, traffic speed forecasting, traffic demand forecasting, and other hybrid traffic forecasting. To support future research in this field, we also provide information on open datasets, source resources, challenges, and potential research directions. As far as we know, this paper represents the first comprehensive survey that focuses specifically on the overall architecture of traffic forecasting. [ABSTRACT FROM AUTHOR]

As the world is moving towards the Internet of Things (IoT), more data rate is required with wider coverage and small delay. While the 5G supports the needed increased data rate, it suffers from a decreased coverage per tower. In contrast, the low population areas such as deserts, oceans, forests, and mountains contain most of the world's transportation such as highways, ships, and planes as well as large areas of agricultural lands that are all not supported by 5G. Consequently, LEO satellite constellations have been introduced to cover the earth's surface with a high-speed data rate. Each LEO satellite constellation requires 4000 + satellites and at least one ground base station (BS) for control, tracking, telemetry, and remote sensing functions. However, to support the needed downlink high-speed data rate demand, the LEO satellite constellation must add many BSs to increase the uplink data rate. As a result, the GEO communication satellites face great competitors in their market. This paper introduces the hybrid LEO-GEO satellite communication system to change this competition into cooperation that benefits both systems. In the hybrid LEO-GEO satellite communication system, this paper proposes maximizing the downlink utilization of both LEO and GEO satellites by the traffic-aware Artificial Expectation Detection (AED) technique. In such a technique, the trending multicast data choose the GEO link while the unicast data and control data choose the LEO link to maximize the downlink utilization efficiency. Our results show that using AED is power efficient and delay efficient while increasing the data rate by (100)x to (100k)x or decreasing the needed number of LEO BSs. [ABSTRACT FROM AUTHOR]

In Vehicular Ad Hoc Networks, reliable information transmission relies on an effective routing strategy. Most existing reinforcement learning-based routing methods are ineffective in dynamic network environments and cannot prevent inefficient network routing. Efficient network routing can be controlled by network traffic management, so this paper proposes an intelligent routing strategy based on Deep Reinforcement Learning to enhance routing performance. By integrating intersection forwarding and traffic awareness capabilities, this paper addresses the problem of local optimality and utilizes the Deep Q Network to make intersection forwarding decisions. The state space of this strategy consists of intersection nodes, road information between intersections, and forwarding packet information. When a vehicle node carrying a packet approaches an intersection based on the state space, the intersection node uses a neural network to select the optimal next-hop relay intersection from past learning experiences. It generates appropriate vehicle routing decisions based on information from the current and candidate relay intersections. Finally, we use the real taxi trajectory data of Beijing City to conduct extensive simulation experiments. Simulation results and analysis demonstrate that the proposed strategy outperforms related research regarding higher average packet delivery ratio, shorter average end-to-end delay, and lower average overhead ratio in dense and sparse traffic periods under real road environments. Consequently, this strategy provides efficient and reliable message transmission services for Vehicular Ad Hoc Networks. [ABSTRACT FROM AUTHOR]

With the increasing growth of information through smart devices, enhancing the quality of human life necessitates the adoption of various computational paradigms including, cloud, fog, and edge in the Internet of Things (IoT) network. Among these paradigms, cloud computing, as an emerging technology, extends cloud layer services to the network edge. This enables resource allocation operations to occur closer to the end user, thereby reducing resource processing time and network traffic overhead. Consequently, the resource allocation problem for cloud service providers, in terms of presenting a suitable platform using computational paradigms, is considered a challenge. Also, Energy Efficiency, Heterogeneity, and Scalability are problems of the cloud computing environment. To solve these problems, resource allocation approaches are divided into two methods: auction-based methods (aimed at increasing profits for service providers while ensuring user satisfaction and usability) and optimization-based methods (focused on energy, cost, network exploitation, runtime, and time delay reduction). Hence, this paper presents a comprehensive literature study (CLS) on artificial intelligence methods such as machine and deep learning for resource allocation optimization in computing environments, such as cloud computing, fog computing, and edge computing. Since deep learning methods are widely used in resource allocation problems, this paper also explores resource allocation approaches based on deep learning techniques, such as deep reinforcement learning, Q-learning, reinforcement learning, and online learning, as well as classical learning methods like Bayesian learning. As a main important achievement, the use of deep reinforcement learning-based methods has increased in the fog paradigm in the past few years. [ABSTRACT FROM AUTHOR]

Distributed Denial of Service (DDoS) attack is a major threat to the Internet of Things (IoT), Software Defined Networks (SDN), and Cloud Computing Networks. Due to the tremendous applications of IoT networks, the number of DDoS attacks is increasing significantly, and most sophisticated DDoS attacks are generated through IoT botnets. An IoT botnet-based DDoS attack can disrupt the network quickly with a surge of malicious traffic. Especially in an SDN network, it is important to detect the DDoS attack before it occurs to the SDN controller. DDoS attacks on the centralized controller of the SDN can disrupt the whole network. So, identifying DDoS attacks at the earliest is a critical security measure for network experts and practitioners. In this paper, we analyze the DDoS attack on an SDN environment and develop a method to identify the DDoS attack using Deep Learning (DL). The proposed method converts the captured raw network traffic to image data and classifies the malicious data from normal data. The method is evaluated on our test-bed simulated dataset and two other benchmark datasets. The experimental comparison shows that the proposed method performs better on all three datasets, giving more than 99% classification accuracy. [ABSTRACT FROM AUTHOR]

Big data becomes the key for ubiquitous computing and intelligence, and Distributed Storage Systems (DSS) are widely used in large-scale data centers or in the cloud for efficient data management. However, the data on stored are likely to be unavailable due to hardware failures and cyberattacks, e.g. DDoS. Maximum Distance Separable (MDS) codes are commonly used for the recovery of faulty storage nodes or unavailable data. However, the recovery of data nodes usually involves access to multiple nodes, which introduces significant communication overheads to the DSS. In this paper, a new DSS based on the Redundant Residue Number System (RRNS) is proposed, where efficient recovery is enabled by applying the second version of Chinese Remainder Theorem (CRT-II). The complexity and network traffic of the proposed data protection scheme is analyzed theoretically and compared with that of traditional MDS based DSSs. Experimental results show that the proposed DSS achieves lower encoding complexity, lower recovery complexity and lower network traffic than the MDS based schemes. Although the proposed data protection scheme introduces computation overheads for the case on which there are no failing nodes, its complexity is still lower for scenarios with frequent data updates. In addition, the proposed scheme introduces additional advantages in terms of security and storage flexibility. [ABSTRACT FROM AUTHOR]

With the rapid development of network technology, the Internet has brought significant convenience to various sectors of society, holding a prominent position. Due to the unpredictable and severe consequences resulting from malicious attacks, the detection of anomalous network traffic has garnered considerable attention from researchers over the past few decades. Accurately labeling a sufficient amount of network traffic data as a training dataset within a short period of time is a challenging task, given the rapid and massive generation of network traffic data. Furthermore, the proportion of malicious attack traffic is relatively small compared to the overall traffic data, and the distribution of traffic data across different types of malicious attacks also varies significantly. To address the aforementioned challenges, this paper presents a novel network anomaly detection algorithm based on semi-supervised learning and adaptive multiclass balancing. Building upon the assumption of consistent distribution between labeled and unlabeled data, this paper introduces the multiclass split balancing strategy and the adaptive confidence threshold function. These innovative approaches aim to tackle the issue of the multiclass imbalanced in traffic data. By leveraging the mutually beneficial relationship between semi-supervised learning and ensemble learning, this paper presents the collaborative rotation forest algorithm. This algorithm is specifically designed to enhance performance of anomaly detection in an environment with label inadequacy. Several comparative experiments conducted on the NSL-KDD, UNSW-NB15, and ToN-IoT demonstrate that the proposed algorithm achieves significant improvements in performance. Specifically, it enhances precision by 1.5–5.7%, recall by 1.5−5.7%, and F-Measure by 1.4−4.3% compared to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Traffic sign poles are crucial components of the highway system, and their maintenance mainly relies on the subjective judgment of staff, which is low-efficiency and will lead to mistaken inspections. This paper proposes a convenient and effective method to monitor the health of sign poles by using a mobile phone. It is unknown whether a mobile phone can be used as a conventional acceleration sensor. Therefore, the performance of the mobile phone was initially tested to ensure its suitability for acquiring the acceleration data of the traffic sign pole. The results show that the acceleration sensor of mobile phones is high-performance and can be used as a traditional acceleration sensor under the similar sampling frequency. The mobile phone can measure the 1st, 2nd and 5th natural frequencies of the traffic sign pole. Although the 1st and 5th natural frequencies have a large error with simulation, the 2nd natural frequency is accurate and stable. The looseness of the base connection greatly impacts on the 2nd natural frequency, which can be used as a measure of the loose base connection. The 2nd natural frequency was measured for 21 times and found to conform to a normal distribution. The relationship between the 2nd natural frequency and base connection loosening was investigated, which fitted to the equation. The fitting result is good and can be used to predict the degree of sign pole base connection loosening. Therefore, the mobile phone based convenient health monitoring method for traffic sign poles is feasible. [ABSTRACT FROM AUTHOR]

This work is devoted to the analysis for a new class of set-valued inverse mixed variational inequalities (SIMVIs) in reflexive Banach spaces, when both the mapping and the constraint set are perturbed simultaneously by two parameters. Several equivalence characterizations are given for SIMVIs to have nonempty and bounded solution sets. Based on the equivalence conditions, under the premise of monotone mappings, the stability result for the SIMVIs is obtained in the reflexive Banach space. Furthermore, to illustrate the results, an example of the traffic network equilibrium control problem is provided at the end of this paper. The results presented in this paper generalize and extend some known results in this area. [ABSTRACT FROM AUTHOR]

In recent years, surveillance systems have significantly increased in various environments. The Internet of Things (IoT), which interconnects objects through the Internet, enhances the capabilities of these systems. Typically, in IoT-enabled surveillance systems, the recorded videos should be processed and stored in cloud servers, which increases the system response time. Sending the videos to a cloud data center through the infrastructure network may also cause performance issues such as delay, jitter, and packet loss. Usually, edge computing is a viable solution to overcome these problems. This paper proposes a deep learning-based network traffic management policy for IoT-enabled surveillance systems in which the user's quality of experience (QoE) is a performance metric. For this purpose, first, a host in the edge data center preprocesses the video to reduce the video file size. Next, we designed a QoE-aware routing algorithm that uses a minimum delay-maximum mean opinion score heuristic algorithm to create a dataset that contains optimal routes with high QoE values. Then, it trains a sequential deep recurrent neural network (deep-RNN) on this dataset, and the trained model predicts the optimal routes between a source edge data center and a destination cloud center. Because software-defined networks (SDN) facilitate the execution of intelligent algorithms in networks, we implemented the proposed policy in the SDN controller of the infrastructure network. The simulation results showed that the proposed method could reduce the end-to-end packet loss ratio and increase the QoE values in IoT-enabled surveillance systems. Furthermore, converting the video codec on the edge layer reduces the video size, reducing the network latency by 50%. The experimental results also showed 94% accuracy using the proposed sequential deep RNN model. [ABSTRACT FROM AUTHOR]

Real-time and accurate traffic flow forecasting plays a crucial role in transportation systems and holds great significance for urban traffic planning, traffic management, traffic control, and more. The most difficult challenge is the extraction of temporal features and spatial correlations of nodes in traffic flow forecasting. Meanwhile, graph convolutional networks has shown good performance in extracting relational spatial dependencies in existing methods. However, it is difficult to accurately mine the hidden spatial-temporal features of the traffic network by using graph convolution alone. In this paper, we propose a multi-attention gated temporal graph convolution network (MATGCN) for accurately forecasting the traffic flow. Firstly, we propose a gated multi-modal temporal convolution(MTCN) to handle the long-term series of the raw traffic data. Then, we use an efficient channel attention module(ECA) to extract temporal features. For the complexity of the spatial structure of traffic roads, we develop multi-attention graph convolution module (MAGCN)including graph convolution and graph attention to further extract the spatial features of a road network. Finally, extensive experiments are carried out on several public traffic datasets, and the experimental results show that our proposed algorithm outperforms the existing methods. [ABSTRACT FROM AUTHOR]

With the immense growth of the internet, sensitive, confidential, important corporate and individual data passing through the internet has grown rapidly. Due to the limitation of security systems, potential hackers and attackers have possessed vulnerabilities and attacks for intruding into the network to gain confidential and sensitive information to affect the performance of networks by breaching network confidentiality. Thereby, to counterfeit these attacks and abnormal behaviors, a network intrusion detection system (NIDS), acts as a crucial branch of cybersecurity for analysis and monitoring the network traffic regularly to report and detect abnormal and malicious activities in a network. Currently, various reviews and survey papers have covered various techniques for NIDS, out of which, mostly followed a non-systematic way of approach without an in-depth analysis of techniques and evaluation metrics used by deep learning(DL) based NIDS models. In addition, various reviews focused on machine learning (ML) and DL-based methodology, but with less emphasis on DL techniques (i.e. AE, CNN, DNN, DBN, RNN, and Hybrid DL) based classification. Thereby, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was used to accomplish this work by providing a comprehensive and detailed overview of DL-based NIDS. Research papers for this work were collected from five well-known databases (ScienceDirect, IEEE, Hindawi, SpringerNature, and MDPI) which were cut among several reputable conference proceedings and reputable journals. Across the 750 articles identified in the literature, 72 research papers were finally marked and selected for synthesis and analysis to find the answers to research questions. In addition, we identified various potential research challenges in the current domain based on research findings. Lastly, to design an efficient NIDS, we concluded our study by identifying high-impact and promising future research areas in the NIDS domain. [ABSTRACT FROM AUTHOR]

Malware remains a persistent threat to industrial operations, causing disruptions and financial losses. Traditional malware detection approaches struggle with the increasing complexity of false positives and negatives. However, existing Intrusion Detection Systems (IDSs) often lack the capability to assess the severity of detected malware, crucial for effective threat mitigation. This paper presents a novel model, MAlware DEtection and Severity Analysis for eNcrypted Traffic (MADESANT), designed to detect and analyze malware severity in encrypted traffic data. MADESANT combines Deep Learning (DL)-based intrusion detection with Machine Learning (ML)-based severity analysis, specifically customized for the minutiae of IoT systems and assets. Notably, MADESANT introduces a cascading model integrating a Cascading Forward Back Propagation Neural Network (CFBPNN) with the J48 tree to systematically assess risk factors in network traffic. Our assessment, conducted on diverse encrypted datasets including UNSW-NB15, IoT23, and XIIoTID, highlights the remarkable efficacy of MADESANT. Impressively, it achieves a flawless 0% false positive rate in detecting binary attack instances, surpassing benchmarks set by conventional models. Additionally, MADESANT excels in accurately estimate malware severity, providing invaluable insights into the factors contributing to the risk. To further validate its efficiency, we compared MADESANT against prevalent Neural Network models like FeedForward and Recurrent Neural Networks, with MADESANT emerging as the superior choice. The experimentation encompasses both the entire dataset and subsets generated through meticulous risk factor analysis. These results underscore MADESANT's prowess in not only identifying malware but also in evaluating its potential impact, signifying a significant leap forward in industrial cybersecurity. [ABSTRACT FROM AUTHOR]

The proliferation of Internet of Things (IoT) devices has revolutionized various domains, but it has also brought forth numerous security challenges. One of the most concerning threats is the emergence of hybrid attacks, which combine multiple attack vectors to exploit vulnerabilities in IoT networks. Existing security mechanisms often struggle to effectively predict and detect these sophisticated hybrid attacks, leading to compromised system integrity and data confidentiality. In this paper, we propose robust learning approach, named HybridRobustNet (HRN), for predicting and detecting hybrid attacks over IoT networks. HRN integrates machine learning algorithms, deep neural networks, and ensemble techniques to achieve enhanced detection accuracy and resilience against evolving hybrid attack patterns. By leveraging a diverse set of features, including network traffic patterns, device behavior, and communication characteristics, HRN effectively captures the complex relationships and dependencies between various attack components. Furthermore, the proposed approach incorporates real-time adaptive learning mechanisms, enabling it to dynamically adapt to new attack strategies and mitigate false positives. To evaluate the effectiveness of HRN, extensive experiments were conducted on a realistic IoT testbed comprising heterogeneous devices and attack scenarios. The results demonstrate that HRN outperforms state-of-the-art approaches in terms of attack detection accuracy, robustness against evasion techniques, and low false positive rates. Additionally, its computational efficiency makes it suitable for deployment in resource-constrained IoT environments. The contributions of this work are twofold. Firstly, it addresses the pressing need for robust detection mechanisms against hybrid attacks, which can have severe consequences for IoT networks. Secondly, it introduces a unique and adaptive learning approach, HRN, which exhibits superior performance and adaptability in the face of emerging attack strategies. The findings presented in this article provide valuable insights into the design of effective security mechanisms for IoT networks and pave the way for future research in the field of hybrid attack detection. [ABSTRACT FROM AUTHOR]

Identifying and preventing malicious network behavior is a challenge for establishing a secure network communication environment or system. Malicious activities in a network system can seriously threaten users' privacy and potentially jeopardize the entire network infrastructure and functions. Furthermore, cyber-attacks have grown in complexity and number due to the ever-evolving digital landscape of computer and network devices in recent years. Analyzing network traffic using network intrusion detection systems (NIDSs) has become an integral security measure in modern networks to identify malicious and suspicious activities. However, most intrusion detection datasets contain imbalance classes, making it difficult for most existing classifiers to achieve good performance. In this paper, we propose a double-layer feature extraction and feature fusion technique (CNN-GRU-FF), which uses a modified focal loss function instead of the traditional cross-entropy to handle the class imbalance problem in the IDS datasets. We use the NSL-KDD and UNSW-NB15 datasets to evaluate the effectiveness of the proposed model. From the research findings, it is evident our CNN-GRU-FF method obtains a detection rate of 98.22% and 99.68% using the UNSW-NB15 and NSL-KDD datasets, respectively while maintaining low false alarm rates on both datasets. We compared the proposed model's performance with seven baseline algorithms and other published methods in literature. It is evident from the performance results that our proposed method outperforms the state-of-the-art network intrusion detection methods. [ABSTRACT FROM AUTHOR]

With rapid development and deployment of artificial intelligence methods, the Industrial Internet of Things (IIoT) has highly developed to fast tracing industrial communications and optimizing manufacturing processes. In Industry 5.0, hyper-automation process as one of technological trends navigates industrial entities to intelligent devices of the IIoT, cloud computing, smart robotics, smart agile software and embedded components by high complexity and reliability. By increasing data communication in the IIoT environments and cloud computing, the security and safety of hyper-automation process is also increasingly unstable and challengeable with respect to cyber-attacks, unstructured malwares and abnormal activities. With the diversification and unexpected behaviors of cyber-security threats, traditional cyber-attack detection systems have some critical problems with increasing massive data including unappropriated feature selection and extraction, high computation time in prediction and inaccurate classification models. Due to the above-mentioned challenges, this paper presents a new cloud-based cyber-attack detection architecture based on Ensemble Bagged Trees Detection (EBTD) algorithm for predicting malicious behaviors and types of cyber-attacks for hyper-automation process in the IIoT. The proposed architecture uses Analysis of Variance (ANOVA) and a priority-based feature selection and extraction model to find the optimal features with highly dependent on the network traffic, computation time, malicious behaviors, and types of attacks. Then, experimental results are conducted using the technical data sets UNSW-NB15 and NSL-KDD. The simulation results show that the proposed architecture performance performs better than other case studies and prediction models and effective on optimization of large-scale cyber-attack detection systems for critical hyper-automation process in the IIoT environment. [ABSTRACT FROM AUTHOR]

The Intrusion Detection System (IDS) distinguishes the harmful entries from the normal ones in network traffic data and aids in network security. Due to the emergence of new and unknown network-connected devices, a lot of modern systems were penetrated. As a result, it is critical to improve information security and to detect new cyber-attacks exploiting various application protocols such as Hyper Text Transfer Protocol (HTTP) and Domain Name System (DNS). Therefore, this paper introduced an Optimized Bidirectional Convolutional Neural Network and Long-Short term Memory (OBCLSTM) method to detect whether the protocol HTTP and DNS is attacked or not. Initially, the records are fed to data normalization and data encoding. After pre-processing, the vectors are fed to the OBCLSTM model. The Bidirectional Channel Pooling (BiCP) layer is used to learn behavior-based features (which show interactions among hosts based on ports, destinations and behavior) and flow-based features (which identify basic flows, such as IP addresses of source-destination and ports), which improves the accuracy of detecting malicious attacks. In the OBCLSTM model, the best hyper parameter configuration for Convolutional Neural Network (CNN) to learn features is tuned using Enhanced Red Fox Optimization (ERFO). Then, bidirectional long short-term memory (BiLSTM) is used to extract features in the time domain and has the ability to preserve the long-term of the information from historical context, allowing attackers to be detected early before causing widespread damage to networks. Finally, the fully connected layer utilizes these features to classify the network data as attacks (types of attacks) or normal. Tests are conducted on the NSL-KDD99, TUIDS, UNSW-NB15 and BoT-IoT datasets. The proposed OBCLSTM method attains better performance in terms of precision, accuracy, recall, and F-measure. [ABSTRACT FROM AUTHOR]

The increasing complexity and adversity of cyber-attacks have prompted discussions in the cyber scenario for a prognosticate approach, rather than a reactionary one. In this paper, a signature-based intrusion detection system has been built based on C5 classifiers, to classify packets into normal and attack categories. Next, an anomaly-based intrusion detection was built based on the LSTM (Long-Short Term Memory) algorithm to detect anomalies. These anomalies are then fed into the signature generator to extract attributes. These attributes get uploaded into the C5 training set, aiding the ensemble model in continual learning with expanding signatures of unknown attacks. By generating signatures of unknown attacks, the self-healing attribute of the ensemble model contributes to the early detection of attacks. For the C5 classifier, the proposed model is evaluated on the UNSW-NB15 dataset, while for the LSTM model, it is evaluated on the ADFA-LD dataset. Compared to conventional models, the experimental results show better detection rates for both known and unknown attacks. The C5 classifier achieved a True Positive Rate of 97% while maintaining a false positive rate of 8%. Also, the LSTM model achieved a detection rate of 90% while retaining a 17% False Alarm Rate. As the proposed model learns, its performance in real network traffic also improves and it also eliminates human intervention when updating training data. [ABSTRACT FROM AUTHOR]

Software-defined networking will be a critical component of the networking domain as it transitions from a standard networking design to an automation network. To meet the needs of the current scenario, this architecture redesign becomes mandatory. Besides, machine learning (ML) and deep learning (DL) techniques provide a significant solution in network attack detection, traffic classification, etc. The DDoS attack is still wreaking havoc. Previous work for DDoS attack detection in SDN has not yielded significant results, so the author has used the most recent deep learning technique to detect the attacks. In this paper, we aim to classify the network traffic into normal and malicious classes based on features in the available dataset by using various deep learning techniques. TCP, UDP, and ICMP traffic are considered normal; however, malicious traffic includes TCP Syn Attack, UDP Flood, and ICMP Flood, all of which are DDoS attack traffic. The major contribution of this paper is the identification of novel features for DDoS attack detection. Novel features are logged into the CSV file to create the dataset, and machine learning algorithms are trained on the created SDN dataset. Various work which has already been done for DDoS attack detection either used a non-SDN dataset or the research data is not made public. A novel hybrid machine learning model is utilized to perform the classification. The dataset used by the ML/DL algorithms is a collection of public datasets on DDoS attacks as well as an experimental DDoS dataset generated by us and publicly available on the Mendeley Data repository. A Python application performs the classification of traffic into one of the classes. From the various classifiers used, the accuracy score of 99.75% is achieved with Stacked Auto-Encoder Multi-layer Perceptron (SAE-MLP). To measure the effectiveness of the SDN-DDoS dataset, the other publicly available datasets are also evaluated against the same deep learning algorithms, and traffic classification accuracy is found to be significantly higher with the SDN-DDoS dataset. The attack detection time of 216.39 s also serve as experimental evidence. [ABSTRACT FROM AUTHOR]

The traditional standalone computing approach is difficult to handle the task of processing large XML data due to scalability, thus distributed processing using cluster systems becomes an inevitable choice. The currently distributed XML processing methods generally rely on existing distributed computing frameworks for general purpose data, which have limitations such as complex configuration, inflexible working mechanism, and difficult performance optimization in the context of XML semi-structural features and complex queries. In addition, XML distributed queries suffer from a low level of automatic processing and lack of effective integration with distributed XML parsing and indexing. In this paper we propose an integrated method for distributed processing of large XML data, called the dXML method. Our method supports the distributed parsing of arbitrary XML fragment and the distributed creation of index, and adopts the efficient navigational XPath evaluation based on relation index. Through a distributed XPath evaluation approach based on filter-upon-pre-evaluate, our method enables data locality and reduces network traffic during the distributed evaluation of complex XPath predicates. dXML integrates the distributed processing technology of XML parsing, index creation and XPath query, provides a one-stop XML processing solution, supports the automatic distributed processing of large XML data, and has the characteristics of lightweight configuration and flexible working mechanism. Experimental evaluation verifies the effectiveness of dXML, and comparative experimental results show that dXML has better distributed query performance than both the typical existing navigational and Twig distributed processing methods. [ABSTRACT FROM AUTHOR]

Sensors, wearables, mobile devices, and other Internet of Things (IoT) devices are becoming increasingly integrated into all aspects of our lives. They are capable of gathering enormous amounts of data, such as image data, which can then be sent to the cloud for processing. However, this results in an increase in network traffic and latency. To overcome these difficulties, edge computing has been proposed as a paradigm for computing that brings processing closer to the location where data is produced. This paper explores the merging of cloud and edge computing for IoT and investigates approaches using machine learning for dimensionality reduction of images on the edge, employing the autoencoder deep learning-based approach and principal component analysis (PCA). The encoded data is then sent to the cloud server, where it is used directly for any machine learning task without significantly impacting the accuracy of the data processed in the cloud. The proposed approach has been evaluated on an object detection task using a set of 4000 images randomly chosen from three datasets: COCO, human detection, and HDA datasets. Results show that a 77% reduction in data did not have a significant impact on the object detection task's accuracy. [ABSTRACT FROM AUTHOR]

Cybersecurity has emerged as a critical global concern. Intrusion Detection Systems (IDS) play a critical role in protecting interconnected networks by detecting malicious actors and activities. Machine Learning (ML)-based behavior analysis within the IDS has considerable potential for detecting dynamic cyber threats, identifying abnormalities, and identifying malicious conduct within the network. However, as the number of data grows, dimension reduction becomes an increasingly difficult task when training ML models. Addressing this, our paper introduces a novel ML-based network intrusion detection model that uses Random Oversampling (RO) to address data imbalance and Stacking Feature Embedding based on clustering results, as well as Principal Component Analysis (PCA) for dimension reduction and is specifically designed for large and imbalanced datasets. This model's performance is carefully evaluated using three cutting-edge benchmark datasets: UNSW-NB15, CIC-IDS-2017, and CIC-IDS-2018. On the UNSW-NB15 dataset, our trials show that the RF and ET models achieve accuracy rates of 99.59% and 99.95%, respectively. Furthermore, using the CIC-IDS2017 dataset, DT, RF, and ET models reach 99.99% accuracy, while DT and RF models obtain 99.94% accuracy on CIC-IDS2018. These performance results continuously outperform the state-of-art, indicating significant progress in the field of network intrusion detection. This achievement demonstrates the efficacy of the suggested methodology, which can be used practically to accurately monitor and identify network traffic intrusions, thereby blocking possible threats. [ABSTRACT FROM AUTHOR]

In the present trend of having an explosive use of smartphones and IoT-based devices, there is an exponential growth in mobile data. It has already caused an overburden of mobile data traffic across the network service providers. The cellular networks face an enormous challenge in predicting the need of upcoming technologies specifically the mobile data offloading solutions. Their limitations to evolve all of a sudden restrict the network administrators and collaborators to offer a compromised but promising solution to cater the mobile data requirements. In this paper, we study the evolutionary trend in data offloading solutions since its evolution. We classify the available literature into different types of offloading solutions on the basis of their major contributions, i.e., data offloading delay permissions, decision makers in offloading solutions, types of networks used for offloading and the major infrastructure requirements for offering offloading. Besides, we present the timeline analysis of the mobile data offloading strategies by discussing their pros and cons for their application in real network scenarios. Towards the end, we highlight the major research issues and challenges to showcase the upcoming need of feasible contribution of research community. [ABSTRACT FROM AUTHOR]

Traffic volume propagating from upstream road link to downstream road link is the key parameter for designing intersection signal timing scheme. Recent works successfully used graph convolutional network (GCN) and specific time-series model to forecast traffic flow by capturing the spatial–temporal features. However, accurately predicting traffic propagation flow (tpf) is challenging, since the classical GCN model only considers the influence of adjacent road link. In complex urban road network, specific traffic propagation flow (tpf) is affected by various spatial features, such as adjacent tpf, which influences from tpf with same upstream link and tpf with same downstream link. Thus, we proposed a multi-graph learning-based model named TPP-GCN (traffic propagation prediction-graph convolutional network) in this paper to predict the traffic propagation flow in urban road network. The TPP-GCN model captures not only the temporal features but also multi-spatial features based on multi-layer convolution. We validated the model using real-world traffic flow data derived from taxi GPS data in Shenzhen, China. Finally, we compare and evaluate the proposed model with the existing models across several prediction scales. [ABSTRACT FROM AUTHOR]

As the number of cores in a chip multiprocessor increase, the directory size becomes excessive. Current research shows that directory size can be reduced by tracking private entries with coarse-grain directory entries called region entries. In order to indicate which blocks the region owner has cached, the present vector in the region entries in the dual-grain directory (DGD) uses a bit vector format. If a coarser-grain region granularity is used, the length of the region entries becomes excessive. Besides, most of the latest scalable directories use the short-entry directory format with only one pointer. Therefore, DGD has limited flexibility of region granularity and is incompatible with the latest scalable directories. In this paper, we propose a scalable short-entry dual-grain coherence directory with flexible region granularity (SS-DGD). In private region entries, a counter is used instead of the original bit vector. Region entries using counters and private block entries using a single pointer always have the same length, giving SS-DGD the flexibility of region size. To reduce the directory size, SS-DGD is divided into a shared directory and a private directory that includes private block entries and private region entries. With the same total number of directory entries, SS-DGD has a smaller directory size than previous DGD because the private directory entries in SS-DGD are shorter. And the detailed simulation-based study shows that there are no statistically significant differences in execution time and network traffic between SS-DGD and DGD. In the 64-core system, our proposal can reduce the directory size by 29.9%. More importantly, the region entries in SS-DGD can be used in the latest scalable directories and have a high potential to compress the number of directory entries. [ABSTRACT FROM AUTHOR]

In the cyberspace, device identification has become one of the most important factors in improving security of a network, containing both Internet of Things (IoT) and non-IoT devices. Resource-constraint IoT devices are generally more vulnerable than non-IoT devices, to different kinds of security threats, including Mirai botnet and spoofing attacks. In this paper, a device fingerprinting (DFP) scheme has been proposed based on the analysis of network traffic characteristics. Four statistical features from two device-specific features have been selected using statistical assessment to generate DFP for classification task using a supervised machine learning Random Forest classifier. Experimental results have shown that the proposed DFP scheme is able to classify device type with 99.81% accuracy on the public UNSW dataset, whilst accuracies of 99.50% and 97.10% have been reported for the identification of individual IoT and non-IoT devices, respectively. The proposed DFP scheme has also demonstrated superior performance as compared to other DFP methods in the literature, despite using less number of features and packets for DFP. These signify that the proposed DFP scheme can be used as a network security reinforcement tool in a heterogeneous network environment. [ABSTRACT FROM AUTHOR]

In recent years, intrusion detection systems (IDSs) have increasingly come to be regarded as a significant method due to their potential to develop into a key component that is necessary for the safety of computer networks. This work focuses on the usage of extreme learning machines, which are also known as ELMs, with the purpose of spotting prospective intrusions and assaults. The proposed method combines the self-adaptive differential evolution method for optimising network input weights and hidden node biases and multi-node probabilistic approach with the extreme learning machine for deriving network output weights. This body of work presents an innovative method of learning that can be put into practice in order to determine whether or not an incursion has taken place in the system that is the focus of the investigation that is being carried out by this body of work. A hybrid extreme learning machine is used in the execution of this strategy. When there is one thousand times more traffic on a network, the ability of regular IDS systems to detect malicious network intrusions is lowered by a factor of one hundred. This is because there are less opportunities to detect the intrusions. This is due to the fact that there are less probabilities to identify potential dangers. This paper lays the groundwork for a novel methodology for identifying malicious network breaches. The findings of the simulation demonstrated that putting into practice the approach that was proposed resulted in an improvement in the accuracy of the scenario's classification while it was being investigated. The implementation of the method seems to have produced the desired results. [ABSTRACT FROM AUTHOR]

Traffic flow forecasting is a typical multivariate time series problem that has applications in intelligent transportation systems. It requires the modeling of complicated spatial-temporal dependencies and essential uncertainty regarding a road network and traffic conditions. Recently, some studies have improved their models without prespecified graphs by constructing adaptive matrices or learnable node embedding dictionaries; however, they omitted the semantic correlations among distant regions. In this paper, we propose an adaptive generalized PageRank graph neural network (AGP-GNN) for traffic flow forecasting, which jointly models spatial, temporal, and semantic correlations to adaptively generate hidden graph structures. Specifically, the AGP-GNN mainly includes two key components: 1) an adaptive generalized PageRank (AGP) layer, which dynamically assigns different edge weights to reflect the different correlations between the pairwise nodes; and 2) a relative position-based temporal attention (RPTA) layer, which models the complex correlations among different time steps. Moreover, we design a distance and temporal encoding (DTE) approach to incorporate geographic and temporal information. Experimental results obtained on two real-world datasets demonstrate the effectiveness of the AGP-GNN. [ABSTRACT FROM AUTHOR]

Despite their known security shortcomings, Controller Area Networks are widely used in modern vehicles. Research in the field has already proposed several solutions to increase the security of CAN networks, such as using anomaly detection methods to identify attacks. Modern anomaly detection procedures typically use machine learning solutions that require a large amount of data to be trained. This paper presents a novel CAN dataset specifically collected and generated to support the development of machine learning based anomaly detection systems. Our dataset contains 26 recordings of benign network traffic, amounting to more than 2.5 hours of traffic. We performed two types of attack on the benign data to create an attacked dataset representing most of the attacks previously proposed in the academic literature. As a novelty, we performed all attacks in two versions, modifying either one or two signals simultaneously. Along with the raw data, we also publish the source code used to generate the attacks to allow easy customization and extension of the dataset. [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction information can help traffic managers and drivers make more rational decisions and choices. To make an effective and accurate traffic flow prediction, we need to consider not only the spatio-temporal dependencies between data, but also the temporal correlation between data. However, most existing methods only consider temporal continuity and ignore temporal correlation. In this paper, we propose a multi-modal attention neural network for traffic flow prediction by capturing long-short term sequence correlation (LSTSC). In the model, we employed attention mechanisms to capture the spatio-temporal correlations of the sequences, and the model based on multiple decision forms demonstrated higher accuracy and reliability. The superiority of the model is demonstrated on two datasets, PeMS08 and PeMSD7(M), particularly for long-term predictions. [ABSTRACT FROM AUTHOR]

Intrusion detection systems have been proposed for the detection of botnet attacks. Various types of centralized or distributed cloud-based machine learning and deep learning models have been suggested. However, the emergence of the Internet of Things (IoT) has brought about a huge increase in connected devices, necessitating a different approach. In this paper, we propose to perform detection on IoT-edge devices. The suggested architecture includes an anomaly intrusion detection system in the application layer of IoT-edge devices, arranged in software-defined networks. IoT-edge devices request information from the software-defined networks controller about their own behaviour in the network. This behaviour is represented by communication graphs and is novel for IoT networks. This representation better characterizes the behaviour of the device than the traditional analysis of network traffic, with a lower volume of information. Botnet attack scenarios are simulated with the IoT-23 dataset. Experimental results show that attacks are detected with high accuracy using a deep learning model with low device memory requirements and significant storage reduction for training. [ABSTRACT FROM AUTHOR]

In today's interconnected world, network traffic is replete with adversarial attacks. As technology evolves, these attacks are also becoming increasingly sophisticated, making them even harder to detect. Fortunately, artificial intelligence (AI) and, specifically machine learning (ML), have shown great success in fast and accurate detection, classification, and even analysis of such threats. Accordingly, there is a growing body of literature addressing how subfields of AI/ML (e.g., natural language processing (NLP)) are getting leveraged to accurately detect evasive malicious patterns in network traffic. In this paper, we delve into the current advancements in ML-based network traffic classification using image visualization. Through a rigorous experimental methodology, we first explore the process of network traffic to image conversion. Subsequently, we investigate how machine learning techniques can effectively leverage image visualization to accurately classify evasive malicious traces within network traffic. Through the utilization of production-level tools and utilities in realistic experiments, our proposed solution achieves an impressive accuracy rate of 99.48% in detecting fileless malware, which is widely regarded as one of the most elusive classes of malicious software. [ABSTRACT FROM AUTHOR]

Extracting information from growing networks like social networks has a wide domain of applications. Therefore, many large-scale distributed graph processing systems have been developed. Such systems use some kinds of partitioning to distribute a big graph over machines. Minimizing the number of cutting edges and balancing the load are two general factors that most of the partitioning algorithms try to optimize regardless of the properties of the underlying systems. Although these general factors are necessary, we believe that they are not enough for all systems. In this paper, we investigate common features of block-centric graph processing systems and extract some system-specific core factors that influence on the quality of partitions: minimizing the diameter of the integrated-graph, maximizing the size of integrated vertices, and preventing the skew problem in the size of integrated vertices. The proposed partitioning method, tries to optimize system-specific factors as well as general ones. Evaluating iPartition over real-world graphs showed that it can significantly improve the performance of block-centric graph processing systems in terms of network traffic and synchronization barriers. More specifically, the amount of superstep reduction over frequently-used distributed graph partitioning methods varies from 20 to 75%, based on the type of the graph. The reduction in transmitted messages over the network varies from 10 to 80%. [ABSTRACT FROM AUTHOR]

The deployment of long-term evolution systems in an unauthorized spectrum provided a credible solution to manage accelerating network data traffic. Meanwhile, the distribution of spectrum on unlicensed bands creates severe interaction issues. Therefore, this paper proposed a novel listen-and-talk-based MAC (LAT-MAC) protocol to enhance secondary users' (SUs') access performance by implementing a full-duplex mechanism. The application of full-duplex methods efficiently neglects collision and improves spectrum utilization of unlicensed bands. For cognitive radio networks, utilizing these benefits of a full duplex, the proposed LAT-MAC protocol simultaneously executes transmission processes and spectrum sensing. The cognitive network users sense spectrum utilizing energy detectors. For enhanced energy detection, the type 2 fuzzy cooperative spectrum sensing (T2FCSS) technique is implemented. Moreover, to attain enhanced spectrum sensing performances with the negligence of power-throughput tradeoff and self-interference, the switching algorithm-based cognitive radio network (SA-CRN) is employed. The performances of the proposed protocol are analyzed using different metrics, and evaluation results illustrate that it achieves enhanced performance compared with existing techniques. [ABSTRACT FROM AUTHOR]

The video services that account for the majority of global network traffic consume significant amounts of electricity and network resources to meet the large-scale demand of users. Variations in user interest and social influence lead to high maintenance costs for achieving a dynamic balance between supply and demand, which negatively impacts the sustainable development of video services. In this paper, we propose a social-aware video-sharing solution using demand prediction of epidemic-based propagation in wireless networks (SDPEP). SDPEP constructs a video propagation model based on user "pull" and "push" sharing behaviors and designs an estimation method for calculating the probability of video fetching by investigating user interests and social relationships. SDPEP uses the probability of video fetching to calculate the basic reproduction number during epidemic-based video propagation, predicting user demand during the propagation process. To ensure efficient caching with low-cost adjustments to video distribution, SDPEP employs a caching-based adjustment strategy for distributing videos while maintaining dynamic balance between supply and demand. Extensive testing shows that SDPEP outperforms other state-of-the-art solutions. [ABSTRACT FROM AUTHOR]