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Malware variants are the major emerging threats that face cybersecurity due to the potential damage to computer systems. Many solutions have been proposed for detecting malware variants. However, accurate detection is challenging due to the constantly evolving nature of the malware variants that cause concept drift. Existing malware detection solutions assume that the mapping learned from historical malware features will be valid for new and future malware. The relationship between input features and the class label has been considered stationary, which doesn’t hold for the ever-evolving nature of malware variants. Malware features change dynamically due to code obfuscations, mutations, and the modification made by malware authors to change the features’ distribution and thus evade the detection rendering the detection model obsolete and ineffective. This study presents an Adaptive behavioral-based Incremental Batch Learning Malware Variants Detection model using concept drift detection and sequential deep learning (AIBL-MVD) to accommodate the new malware variants. Malware behaviors were extracted using dynamic analysis by running the malware files in a sandbox environment and collecting their Application Programming Interface (API) traces. According to the malware first-time appearance, the malware samples were sorted to capture the malware variants’ change characteristics. The base classifier was then trained based on a subset of historical malware samples using a sequential deep learning model. The new malware samples were mixed with a subset of old data and gradually introduced to the learning model in an adaptive batch size incremental learning manner to address the catastrophic forgetting dilemma of incremental learning. The statistical process control technique has been used to detect the concept drift as an indication for incrementally updating the model as well as reducing the frequency of model updates. Results from extensive experiments show that the proposed model is superior in terms of detection rate and efficiency compared with the static model, periodic retraining approaches, and the fixed batch size incremental learning approach. The model maintains an average of 99.41% detection accuracy of new and variants malware with a low updating frequency of 1.35 times per month.

Wireless Sensors Networks have been the focus of significant attention from research and development due to their applications of collecting data from various fields such as smart cities, power grids, transportation systems, medical sectors, military, and rural areas. Accurate and reliable measurements for insightful data analysis and decision-making are the ultimate goals of sensor networks for critical domains. However, the raw data collected by WSNs usually are not reliable and inaccurate due to the imperfect nature of WSNs. Identifying misbehaviours or anomalies in the network is important for providing reliable and secure functioning of the network. However, due to resource constraints, a lightweight detection scheme is a major design challenge in sensor networks. This paper aims at designing and developing a lightweight anomaly detection scheme to improve efficiency in terms of reducing the computational complexity and communication and improving memory utilization overhead while maintaining high accuracy. To achieve this aim, one-class learning and dimension reduction concepts were used in the design. The One-Class Support Vector Machine (OCSVM) with hyper-ellipsoid variance was used for anomaly detection due to its advantage in classifying unlabelled and multivariate data. Various One-Class Support Vector Machine formulations have been investigated and Centred-Ellipsoid has been adopted in this study due to its effectiveness. Centred-Ellipsoid is the most effective kernel among studies formulations. To decrease the computational complexity and improve memory utilization, the dimensions of the data were reduced using the Candid Covariance-Free Incremental Principal Component Analysis (CCIPCA) algorithm. Extensive experiments were conducted to evaluate the proposed lightweight anomaly detection scheme. Results in terms of detection accuracy, memory utilization, computational complexity, and communication overhead show that the proposed scheme is effective and efficient compared few existing schemes evaluated. The proposed anomaly detection scheme achieved the accuracy higher than 98%, with O(nd) memory utilization and no communication overhead.

The Internet of Things (IoT) concept has emerged to improve people’s lives by providing a wide range of smart and connected devices and applications in several domains, such as green IoT-based agriculture, smart farming, smart homes, smart transportation, smart health, smart grid, smart cities, and smart environment. However, IoT devices are at risk of cyber attacks. The use of deep learning techniques has been adequately adopted by researchers as a solution in securing the IoT environment. Deep learning has also successfully been implemented in various fields, proving its superiority in tackling intrusion detection attacks. Due to the limitation of signature-based detection for unknown attacks, the anomaly-based Intrusion Detection System (IDS) gains advantages to detect zero-day attacks. In this paper, a systematic literature review (SLR) is presented to analyze the existing published literature regarding anomaly-based intrusion detection, using deep learning techniques in securing IoT environments. Data from the published studies were retrieved from five databases (IEEE Xplore, Scopus, Web of Science, Science Direct, and MDPI). Out of 2116 identified records, 26 relevant studies were selected to answer the research questions. This review has explored seven deep learning techniques practiced in IoT security, and the results showed their effectiveness in dealing with security challenges in the IoT ecosystem. It is also found that supervised deep learning techniques offer better performance, compared to unsupervised and semi-supervised learning. This analysis provides an insight into how the use of data types and learning methods will affect the performance of deep learning techniques for further contribution to enhancing a novel model for anomaly intrusion detection and prediction.

The flexibility of software defined network come at the cost of network rule configuration and load balancing during path selection. Network topology changes more often, similarly a high frequency of network failure was reported. The latter and former events cause devices to carry out rules update operations more frequently (inserting, deleting, and modifying rules). These operations are proportional to the number of rules in the device flowtable. The higher the flowtable utilization the higher the operation. Consequently, increases the chance of switch flowtable overflow. Therefore, upon occurrences of any of the two events, effective path computation is required to guide the routing of flows to the destination. However, the traditional path selection algorithms failed to consider the switch role during the path selection process. The key to address this challenge is to maintain the load balance among switches when selecting a path. Evenly network load distribution help to minimize the chance of network bottleneck in some part of the network. This paper proposed minimal Critical switch-based routing (CSBR) to select a path that can incur light rule update operation. CSBR approach is based on a simple and yet effective critical switch frequency score (CWFscore) metric which encourages path selection with a minimal number of critical switches. The experimental benchmark results show that the proposed algorithm has a better flowtable occupancy rate compared to the baseline work.

The cryptography employed against user files makes the effect of crypto-ransomware attacks irreversible even after detection and removal. Thus, detecting such attacks early, i.e. during pre-encryption phase before the encryption takes place is necessary. Existing crypto-ransomware early detection solutions use a fixed time-based thresholding approach to determine the pre-encryption phase boundaries. However, the fixed time thresholding approach implies that all samples start the encryption at the same time. Such assumption does not necessarily hold for all samples as the time for the main sabotage to start varies among different crypto-ransomware families due to the obfuscation techniques employed by the malware to change its attack strategies and evade detection, which generates different attack behaviors. Additionally, the lack of sufficient data at the early phases of the attack adversely affects the ability of feature extraction techniques in early detection models to perceive the characteristics of the attacks, which, consequently, decreases the detection accuracy. Therefore, this paper proposes a Dynamic Pre-encryption Boundary Delineation and Feature Extraction (DPBD-FE) scheme that determines the boundary of the pre-encryption phase, from which the features are extracted and selected more accurately. Unlike the fixed thresholding employed by the extant works, DPBD-FE tracks the pre-encryption phase for each instance individually based on the first occurrence of any cryptography-related APIs. Then, an annotated Term Frequency-Inverse Document Frequency (aTF-IDF) technique was utilized to extract the features from runtime data generated during the pre-encryption phase of crypto-ransomware attacks. The aTF-IDF overcomes the challenge of insufficient attack patterns during the early phases of the attack lifecycle. The experimental evaluation shows that DPBD-FE was able to determine the pre-encryption boundaries and extract the features related to this phase more accurately compared to related works.

Vehicular Ad Hoc Networks (VANETs) are among the main enablers for future Intelligent Transportation Systems (ITSs) as they facilitate information sharing, which improves road safety, traffic efficiency, and provides passengers’ comfort. Due to the dynamic nature of VANETs, vehicles need to exchange the Cooperative Awareness Messages (CAMs) more frequently to maintain network agility and preserve applications’ performance. However, in many situations, broadcasting at a high rate leads to congest the communication channel, rendering VANET unreliable. Existing broadcasting schemes designed for VANET use partial context variables to control the broadcasting rate. Additionally, CAMs uncertainty, which is context-dependent has been neglected and a predefined fixed certainty threshold has been used instead, which is not suitable for the highly dynamic context. Consequently, vehicles disseminate a high rate of unnecessary CAMs which degrades VANET performance. A good broadcasting scheme should accurately determine which and when CAMs are broadcasted. To this end, this study proposes a Context-Aware Adaptive Cooperative Awareness Messages Broadcasting Scheme (CA-ABS) using combinations of Adaptive Kalman Filter, Autoregression, and Sequential Deep Learning and Fuzzy inference system. Four context variables have been used to represent the vehicular context, namely, individual driving behaviors, CAMs uncertainty, vehicle density, and traffic flow. Kalman Filter and Autoregression are used to estimate and predict the CAMs messages respectively. The deep learning model has been constructed to estimate the CAMs’ uncertainties which is an important context variable that has been neglected in the previous research. Fuzzy Inference System takes context variables as input and determines an accurate broadcasting threshold and broadcasting interval. Extensive simulations have been conducted to evaluate the proposed scheme. Results show that the proposed scheme improves the CAMs delivery ratio and decreases the CAMs prediction errors.

Crypto-ransomware is a type of malware whose effect is irreversible even after detection and removal. Thus, early detection is crucial to protect user files from being encrypted and held to ransom. Several studies have proposed early detection solutions based on the data acquired during the pre-encryption phase of the attacks. However, the lack of sufficient data in the early phases of the attack adversely affects the ability of feature selection techniques in these models to perceive the common characteristics of the attack features, which makes it challenging to reduce the redundant features, consequently decreasing the detection accuracy. Therefore, this study proposes a novel Redundancy Coefficient Gradual Upweighting (RCGU) technique that makes better redundancy–relevancy trade-offs during feature selection. Unlike existing feature significance estimation techniques that rely on the comparison between the candidate feature and the common characteristics of the already-selected features, RCGU compares the mutual information between the candidate feature and each feature in the selected set individually. Therefore, RCGU increases the weight of the redundancy term proportional to the number of already selected features. By integrating the RCGU into the Mutual Information Feature Selection (MIFS) technique, the Enhanced MIFS (EMIFS) was developed. Further improvement was achieved by proposing MM-EMIFS which incorporates the MaxMin approximation with EMIFS to prevent the redundancy overestimation that RCGU could cause when the number of features in the already-selected set increases. The experimental evaluation shows that the proposed techniques achieved accuracy higher than that in related works, which confirms the ability of RCGU to make better redundancy–relevancy trade-offs and select more discriminative pre-encryption attack features compared to existing solutions.

Online news articles are an important source of information for decisions makers to understand the causal relation of events that happened. However, understanding the causality of an event or between events by traditional machine learning-based techniques from natural language text is a challenging task due to the complexity of the language to be comprehended by the machines. In this study, the Knowledge-oriented convolutional neural network (K-CNN) technique is used to extract the causal relation from online news articles related to the South China Sea (SCS) dispute. The proposed K-CNN model contains a Knowledge-oriented channel that can capture the causal phrases of causal relationships. A Data-oriented channel that captures the position information was added to the K-CNN model in this phase. The online news articles were collected from the national news agency and then the sentences which contain relation such as causal, message-topic, and product-producer were extracted. Then, the extracted sentences were annotated and converted into lower form and base form followed by transformed into the vector by looking up the word embedding table. A word filter that contains causal keywords was generated and a K-CNN model was developed, trained, and tested using the collected data. Finally, different architectures of the K-CNN model were compared to find out the most suitable architecture for this study. From the study, it was found out that the most suitable architecture was the K-CNN model with a Knowledge-oriented channel and a Data-oriented channel with average pooling. This shows that the linguistic clues and the position features can improve the performance in extracting the causal relation from the SCS online news articles. Keywords-component; Convolutional Neural Network, Causal Relation Extraction, South China Sea.

Cloud computing plays an essential role as a source for outsourcing data to perform mining operations or other data processing, especially for data owners who do not have sufficient resources or experience to execute data mining techniques. However, the privacy of outsourced data is a serious concern. Most data owners are using anonymization-based techniques to prevent identity and attribute disclosures to avoid privacy leakage before outsourced data for mining over the cloud. In addition, data collection and dissemination in a resource-limited network such as sensor cloud require efficient methods to reduce privacy leakage. The main issue that caused identity disclosure is quasi-identifier (QID) linking. But most researchers of anonymization methods ignore the identification of proper QIDs. This reduces the validity of the used anonymization methods and may thus lead to a failure of the anonymity process. This paper introduces a new quasi-identifier recognition algorithm that reduces identity disclosure which resulted from QID linking. The proposed algorithm is comprised of two main stages: (1) attribute classification (or QID recognition) and (2) QID dimension identification. The algorithm works based on the reidentification of risk rate for all attributes and the dimension of QIDs where it determines the proper QIDs and their suitable dimensions. The proposed algorithm was tested on a real dataset. The results demonstrated that the proposed algorithm significantly reduces privacy leakage and maintains the data utility compared to recent related algorithms.

Multi-Radio Multi-Channel Wireless Mesh Networks (WMNs) have emerged as a scalable, reliable, and agile wireless network that supports many types of innovative technologies such as the Internet of Things (IoT) and vehicular networks. Due to the limited number of orthogonal channels, interference between channels adversely affects the fair distribution of bandwidth among mesh clients, causing node starvation in terms of insufficient bandwidth, which impedes the adoption of WMN as an efficient access technology. Therefore, a fair channel assignment is crucial for the mesh clients to utilize the available resources. However, the node starvation problem due to unfair channel distribution has been vastly overlooked during channel assignment by the extant research. Instead, existing channel assignment algorithms either reduce the total network interference or maximize the total network throughput, which neither guarantees a fair distribution of the channels nor eliminates node starvation. To this end, the Fairness-Oriented Semi-Chaotic Genetic Algorithm-Based Channel Assignment Technique (FA-SCGA-CAA) was proposed in this paper for Nodes Starvation Problem in Wireless Mesh Networks. FA-SCGA-CAA optimizes fairness based on multiple-criterion using a modified version of the Genetic Algorithm (GA). The modification includes proposing a semi-chaotic technique for creating the primary chromosome with powerful genes. Such a chromosome was used to create a strong population that directs the search towards the global minima in an effective and efficient way. The outcome is a nonlinear fairness oriented fitness function that aims at maximizing the link fairness while minimizing the link interference. Comparison with related work shows that the proposed FA_SCGA_CAA reduced the potential nodes starvation by 22% and improved network capacity utilization by 23%., Comment: 18 pages, 10 Figures

Vehicular ad hoc networks (VANETs) play an important role as enabling technology for future cooperative intelligent transportation systems (CITSs). Vehicles in VANETs share real-time information about their movement state, traffic situation, and road conditions. However, VANETs are susceptible to the cyberattacks that create life threatening situations and/or cause road congestion. Intrusion detection systems (IDSs) that rely on the cooperation between vehicles to detect intruders, were the most suggested security solutions for VANET. Unfortunately, existing cooperative IDSs (CIDSs) are vulnerable to the legitimate yet compromised collaborators that share misleading and manipulated information and disrupt the IDSs&rsquo, normal operation. As such, this paper proposes a misbehavior-aware on-demand collaborative intrusion detection system (MA-CIDS) based on the concept of distributed ensemble learning. That is, vehicles individually use the random forest algorithm to train local IDS classifiers and share their locally trained classifiers on-demand with the vehicles in their vicinity, which reduces the communication overhead. Once received, the performance of the classifiers is evaluated using the local testing dataset in the receiving vehicle. The evaluation values are used as a trustworthiness factor and used to rank the received classifiers. The classifiers that deviate much from the box-and-whisker plot lower boundary are excluded from the set of the collaborators. Then, each vehicle constructs an ensemble of weighted random forest-based classifiers that encompasses the locally and remotely trained classifiers. The outputs of the classifiers are aggregated using a robust weighted voting scheme. Extensive simulations were conducted utilizing the network security laboratory-knowledge discovery data mining (NSL-KDD) dataset to evaluate the performance of the proposed MA-CIDS model. The obtained results show that MA-CIDS performs better than the other existing models in terms of effectiveness and efficiency for VANET.

Most of Vehicular Ad Hoc Network’s (VANET) and Fly Ad Hoc Network (FANET) applications, protocols, and service rely on cooperativeness among network participants. Vehicles, could be unmanned aerial vehicles (e.g. drones), share their sensors’ information to improve network performance and provide safety, traffic efficiency, and entertainments. The accuracy and reliability of this information are vital for VANET performance. However, misbehaving (or faulty) vehicles, which exploit the cooperativeness feature by sharing bogus information, can disrupt the fundamental operations of any potential application and cause loss of people lives and properties. Unfortunately, existing detection approaches cannot effectively thwart such attacks. The highly dynamic vehicular context has been vastly neglected as well as they rely on predefined and static security perimeters to differentiate between false and correct information. In this paper, a context-aware detection approach is proposed to improve the detection accuracy and reduce the false alarm rate. Firstly, the features that well represent the vehicular context has been extracted. Then, an online unsupervised learning method namely the hierarchical clustering algorithm is used to classify the received messages into either genuine or spurious. Finally, Bayesian-based hypothesis testing has been used to confirm the validity of the classification. Results show that the proposed detection approach is promising in effectively detect bogus information attack and improve applications performance.

Software Defined Networking (SDN) enables flexible fine-grained networking policies by allowing the SDN controller to install packet handling rules on distributed switches. The behavior of the general network is at the OpenFlow switch which strongly depends on the set of forwarding entries installed at the switch flow table. As the number of traffic flows increase, the number of flow entries augments. Unfortunately, the current switch flow table memory (TCAM) cannot accommodate the number of required entries and eventually switch begins to evict entries from the flow table. Consequently, throttle flow set up request thereby increased the processing load on the controller. Thus, cause the SDN controller scalability problem which decreases the number of switches that could be managed. Since the flow setup requests are generated due to inappropriate timeout setting and premature eviction of flow entries. This paper addresses the SDN scalability problem and proposes a timeout algorithm called idle-hard timeout allocation (IHTA). The algorithm combines both timeouts dynamically according to the traffic pattern, based on Packet inter-arrival time. Therefore, the IHTA timeout dynamically allocates both timeouts with different values to different traffic flows. Flow entry is evicted when they have no packet expected at a sample time. As a result, the experimental result improved the SDN controller scalability problem thereby reducing excessive flow set-up requests. The benchmark results show that the proposed algorithm reduces the number of packet-in message to 35.2% on average.

Dementia and Alzheimer’s disease are caused by neurodegeneration and poor communication between neurons in the brain. So far, no effective medications have been discovered for dementia and Alzheimer’s disease. Thus, early diagnosis is necessary to avoid the development of these diseases. In this study, efficient machine learning algorithms were assessed to evaluate the Open Access Series of Imaging Studies (OASIS) dataset for dementia diagnosis. Two CNN models (AlexNet and ResNet-50) and hybrid techniques between deep learning and machine learning (AlexNet+SVM and ResNet-50+SVM) were also evaluated for the diagnosis of Alzheimer’s disease. For the OASIS dataset, we balanced the dataset, replaced the missing values, and applied the t-Distributed Stochastic Neighbour Embedding algorithm (t-SNE) to represent the high-dimensional data in the low-dimensional space. All of the machine learning algorithms, namely, Support Vector Machine (SVM), Decision Tree, Random Forest and K Nearest Neighbours (KNN), achieved high performance for diagnosing dementia. The random forest algorithm achieved an overall accuracy of 94% and precision, recall and F1 scores of 93%, 98% and 96%, respectively. The second dataset, the MRI image dataset, was evaluated by AlexNet and ResNet-50 models and AlexNet+SVM and ResNet-50+SVM hybrid techniques. All models achieved high performance, but the performance of the hybrid methods between deep learning and machine learning was better than that of the deep learning models. The AlexNet+SVM hybrid model achieved accuracy, sensitivity, specificity and AUC scores of 94.8%, 93%, 97.75% and 99.70%, respectively.

Software-defined network (SDN) is a new paradigm that decouples the control plane and data plane. This offered a more flexible way to efficiently manage the network. However, the increasing number of traffics due to the proliferation of the Internet of Things (IoT) devices also increase the number of flow arrival which in turn causes flow rules to change more often, and similarly, path setup requests increased. These events required route path computation activities to take place immediately to cope with the new network changes. Searching for an optimal route might be costly in terms of the time required to calculate a new path and update the corresponding switches. However, the current path selection schemes considered only single routing metrics either link or switch operation. Incorporating link quality and switch’s role during path selection decisions have not been considered. This paper proposed Route Path Selection Optimization (RPSO) with multi-constraint. RPSO introduced joint parameters based on link and switches such as Link Latency (LL), Link Delivery Ratio (LDR), and Critical Switch Frequency Score (CWFscore). These metrics encourage path selection with better link quality and a minimal number of critical switches. The experimental results show that the proposed scheme reduced path stretch by 37%, path setup latency by 73% thereby improving throughput by 55.73%, and packet delivery ratio by 12.5% compared to the baseline work.

Wireless Mesh Networks (MR-WMNs) are characterized by having multiple radio interfaces with which, node can send/receive data to/from multiple nodes. This feature increases the network capacity and improves data delivery rate. However, interference between the co-located channels is one of the main constraints that hinder such networks from achieving the optimal utilization of the available resources such as bandwidth and transmission routes. Bottleneck is one of the consequences of such limitation, which leads to network fragmentation and performance degradation especially in the critical links shared by multiple paths connecting the nodes with the gateway. Although several studies have been conducted to address such limitation, they were all built on the premise of stationary topology, which does not hold for real world WMNs that include mobile users in addition to backbone nodes and routers. To this end, this paper proposes a user mobility-aware channel assignment algorithm based on weighted link ranking to fairly allocate the channels and minimizes the interference, thus improves the capacity of the network. Multiple criteria were used to rank those links before applying the channel assignment algorithm. Those criteria were obtained from traffic and network topology such as distance from the gateways, interference index, and traffic load. The results from numerical simulation demonstrate that the proposed scheme has reduced the interference which, consequently, improved the network capacity.

OppNets is a subset of MANET in which there is no point to point connection between devices. Despite challenges of intermittent connectivity and unpredictable mobility characteristics, mobile nodes need to communicate and share valuable information without infrastructure. The routing strategy in OppNet follows a store-carry-forward paradigm, in which a node store messages in its buffer, carry the messages as it moves about its daily activities and opportunistically relay the messages one after another upon meeting their target destinations. This paper presents a movement pattern extraction, a new approach for extracting regular movement pattern of nodes from existing traces to predict a node future location. Human movement is predictable with a high degree of certainty that people visit certain locations and have the opportunity to meet other people on a regular basis. The proposed method incorporate message lifetime and previous history of encounter to extract locations from the GPS trace with respect to message lifetime using Neural Network to predict future location visit of nodes. Prediction method can be used in geocast routing schemes to determine the node’s contribution in forwarding a message to a destination location. Experiment result indicates that the proposed method achieves significant performance over existing methods in terms of accuracy and stability in prediction.

Developments in remote sensing technology have led to a continuous increase in the volume of remote-sensing data, which can be qualified as big remote sensing data. A wide range of potential applications is using these data including land cover classification, regional planning, catastrophe prediction and management, and climate-change estimation. Big remote sensing data are characterized by different types of resolutions (radiometric, spatial, spectral, and temporal), modes of imaging, and sensor types, and this range of options often makes the process of analyzing and interpreting such data more difficult. In this paper, which is the first study of its kind, we propose a novel distributed deep learning-based approach for the classification of big remote sensing images. Specifically, we propose Distributed Convolutional-Neural-Networks for handling RS image classification (RS-DCNN). The first step is to prepare the training dataset for RS-DCNN. Then, to ensure a data-parallel training on the top of the Apache Spark framework, a pixel-based convolutional-neural-network model across the big data cluster is performed using BigDL. Experiments are conducted on a real dataset covering many regions of Saudi Arabia and the results demonstrate high classification accuracy at a faster speed than other state-of-the-art classification methods.

Human height measurement can be achieved by using contact or non-contact techniques. Contact technique is the traditional measuring method which required human resources to perform the measurement. In contrast, for non-contact technique, several kinds of research for measurement have been conducted, mostly with image-processing methods and only a few with the Augmented Reality (AR) approach. The current measuring approaches mostly required external hardware such as laser pointer or artificial fiducial such as 2D markers. In this paper, the world tracking technique and Visual Inertial Odometry is the method used to estimate the human height. The main aim of this paper is to accurately estimate the human height using augmented reality (non-contacted measurements). The methodology used the Apple ARKit plugin, which is the software development tools to build an augmented reality application for IOS device. An algorithm was designed by using Golden Ratio rules to estimate human height from the lower part of human knee; The estimation result is displayed using AR technology to allow the justification of the accuracy of the result. The application is tested with four different measuring methods. The normal full-height measurement result had a 1.13cm (0.73%) bias and a 1.34cm (0.88%) Root Mean Square Error (RMSE); the self-full height measurement had a result of 0.89cm (0.58%) bias and a 1.27cm (0.83%) RMSE; the normal height estimation from the lower part of knee measurement had a result of 0.12cm (0.06%) bias and a 1.34cm (0.89%) RMSE; the self-height estimation from the lower part of knee measurement had a result of 0.15cm (0.09%) bias and a 1.04cm (0.66%) RMSE. The results show that the mobile phone with VIO can be a potential tool for obtaining accurate measurements of human height.

Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches’ flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning.

Vehicular ad hoc network (VANET) is the key enabler for future intelligent transportation systems' applications. Due to its high mobility, VANETs rely on the availability of accurate and reliable mobility information of the vehicles. However, misbehavior in mobility can lead to catastrophic results in both safety and traffic efficiency. Several drawbacks of existing misbehavior detection models designed for VANETs which impacted the performance of the applications and the security solutions altogether. Machine learning has not been studied extensively in misbehavior detection in VANET. In this paper, an effective misbehavior detection model based on machine learning techniques is proposed. The proposed model consists of four main phases: data acquisition, data sharing, analysis and decision making. New features are derived which represent the misbehavior, environment and communication status in order to effectively detect the misbehavior data. By using Artificial Neural Network (ANN) techniques namely the feed forward and the back propagation algorithms, an effective misbehavior classifier is trained based on historical data that contains both attacker and normal traffic data. A real-world traffic dataset namely NGSIM is used to construct and evaluate the proposed model. Results show significant improvement in the effectiveness of the proposed model in comparison with the existing baseline model.

Availability of accurate and continuous positioning information is a fundamental requirement for Vehicular Ad Hoc Networks. However, existing positioning approaches does not fulfill the required accuracy of many VANET’s applications and services. Integrating GPS with vehicles’ kinematic information (GPS/DR) is widely suggested for vehicular positioning. In many cases where the GPS is unavailable or instable for long time, this integration resulted in inaccurate positioning. Recently, cooperative positioning (CP) based on vehicle-to-vehicle (V2V) communication have been proposed as an alternative for GPS/DR in many ad hoc networks. Even though, CP needs high communication to achieve the required accuracy, which is not guaranteed in VANET harsh environment. In this paper, two-stage integration algorithm is proposed to ensure continuous and accurate positioning information. The proposed algorithm integrates GPS and kinematic sensors measurements, as well as neighboring mobility information based on two cascaded Kalman filters (KF). The first KF is used to integrate GPS information with kinematic sensors measurements (dead reckoning). Meanwhile, the second KF is used to integrate the results of the first KF with the neighboring mobility information. Results show that the proposed algorithm outperformed the conventional integration algorithm in terms of positioning accuracy under the tested scenario.

Electrocardiogram (ECG) is beneficial to diagnose various heart diseases. Recently, mobile ECG has received the attention of researchers and practitioners for real time monitoring and automatic diagnosis. However, the performance of ECG’s monitoring system is degraded because of many disturbances created by subject movement, which leads to wrong diagnosis. Several attempts have been performed to remove the noise from clinical ECG signal using various digital signal processing techniques. Those techniques are not directly appropriate to be used for the mobile ECG environment and expected noises. Ultimately, motion artifact still is an open issue in mobile ECG. In this paper, an algorithm is proposed to reduce motion artifact using three sequential adaptive noise filters based on least mean square errors and reference noise estimation methods. The proposed algorithm is evaluated using real dataset that was collected while the subject performing different activities. Results show promising enhancement in the ECG signal quality.

Life-saving decisions in vehicular ad hoc networks (VANETs) depend on the availability of highly accurate, up-to-date, and reliable data exchanged by neighboring vehicles. However, spreading inaccurate, unreliable, and false data by intruders create traffic illusions that may cause loss of lives and assets. Although several solutions for misbehavior detection have been proposed to address these issues, those solutions lack adequate representation and the adaptability to vehicular context. The use of predefined static thresholds and lack of comprehensive context representation have rendered the existing solutions limited to specific scenarios and attack types, which impedes their generalizability. This paper addresses these limitations by proposing an ensemble-based hybrid context-aware misbehavior detection system (EHCA-MDS) model. EHCA-MDS has been developed in four phases, as follows. The static thresholds have been replaced by dynamic ones created on the fly by analyzing the spatial and temporal properties of the mobility information collected from neighboring vehicles. Kalman filter-based algorithms were used to collect the mobility information of neighboring vehicles. Three sets of features were then derived, each of which has a different perspective, namely data consistency, data plausibility, and vehicle behavior. These features were used to construct a dynamic context reference using the Hampel filter. The Hampel-based z-score was used to evaluate the vehicles based on their behavioral activities, data consistency, and plausibility. For comprehensive features representation, multifaceted, non-parametric-based statistical classifiers were constructed and updated online using a Hampel filter-based algorithm. For accurate representation, the output of the statistical classifiers, vehicles’ scores, context reference parameters, and the derived features were used as input to an ensemble learning-based algorithm. Such representation helps to identify the misbehaving vehicles more effectively. The proposed EHCA-MDS model was evaluated in the presence of different types of misbehaving vehicles under different context scenarios through extensive simulations, utilizing a real-world traffic dataset. The results show that the accuracy and robustness of the proposed EHCA-MDS under different vehicular dynamic context scenarios were higher than existing solutions, which confirms its feasibility and effectiveness to improve the performance of VANET critical applications.

Vehicle Ad hoc Network (VANET) is an emerging and promising technology for the Intelligent Transportation System (ITS). VANET can help to increase safety and traffic efficiency in flexible and feasible way. However, disseminating incorrect information in VANET has wide range of implications effecting drivers’ behaviors and causing serious, and may be catastrophic, results. Misbehaving attackers can create traffic illusion to disturb VANET operations as well as the potential deployment of safety and traffic efficiency applications. In this paper, a holistic view of the existing misbehavior detection approaches for countermeasures against spreading malicious data in VANET is studied. In addition, the importance and the challenges faced when verifying the correctness of VANET messages are discussed. Finally, the drawbacks of existing detection and verification approaches are analyzed.

Vehicular Ad-hoc Networks (VANETs) can make roads safer, cleaner, and smarter. It can offer a wide range of services, which can be safety and non-safety related. False or bogus information is a real threat in VANET's safety applications, security and privacy. Vehicles or drivers may react to false information and cause serious problems. In VANETs Drivers' behavioral tendencies can be reflected in the mobility patterns of the vehicles. Monitoring mobility patterns of the vehicles within their transmission range, helps them in earlier detection of the correctness of the received messages. Detection of false messages is not enough to enhance the security and safety. Misbehaving vehicles need to be detected and penalized, so that they can not misbehave in the future. Existing misbehavior detection schemes have not adequately addressed this issue in the highway. In this paper we present a misbehavior detection scheme (MDS) and framework based on the mobility patterns analysis of the vehicles in the vicinity of concerned vehicles. The proposed MDS is a hybrid mechanism of both Data-Centric and Entity-Centric to cover wide range of misbehaviors. Simulation results demonstrate the potential of the proposed MDS and framework especially in highway safety applications.

Vehicular Ad-hoc Network (VANET) can improve road safety through collision avoidance. False or bogus information is a real threat in VANET’s safety applications. Vehicles or drivers may react to false information and cause serious problems. In VANETs drivers’ behavioral tendencies can be reflected in the mobility patterns of the vehicles. Monitoring mobility patterns of the vehicles within their transmission range helps them in earlier detection of the correctness of the received message. This paper presents a misbehavior detection scheme (MDS) and corresponding framework based on the mobility patterns analysis of the vehicles in the vicinity of concerned vehicles. Initial simulation results demonstrate the potential of the proposed MDS and framework in message’s correctness detection, hence its corresponding applications in collision avoidance.

Securing the Vehicular Adhoc Networks (VANETs) along with appropriate protection of the privacy of drivers or vehicle owners is a very challenging task. Drivers' behavioral tendencies can be reflected in the mobility patterns of the vehicles (rational behavior). Misbehaving vehicles are always threat to safety, VANETs security and privacy. Detection of misbehavior can enhance security and privacy in VANETs. This paper is presenting a mobility pattern based misbehavior detection approach in VANETs. Simulation results show that the Location Anonymous Message based proposed approach has the potential to improve security and maintain privacy in VANETs.