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Software-defined networking (SDN) and network functions virtualisation (NFV) are crucial technologies for integration in the fifth generation of cellular networks (5G). However, they also pose new security challenges, and a timely research subject is working on intrusion detection systems (IDSs) for 5G networks. Current IDSs suffer from several limitations, resulting in a waste of resources and some security threats. This work proposes a new three-layered solution that includes forwarding and data transport, management and control, and virtualisation layers, emphasising distributed controllers in the management and control layer. The proposed solution uses entropy detection to classify arriving packets as normal or suspicious and then forwards the suspicious packets to a centralised controller for further processing using a self-organising map (SOM). A dynamic OpenFlow switch relocation method is introduced based on deep reinforcement learning to address the unbalanced burden among controllers and the static allocation of OpenFlow switches. The proposed system is analysed using the Markov decision process, and a Double Deep Q-Network (DDQN) is used to train the system. The experimental results demonstrate the effectiveness of the proposed approach in mitigating DDoS attacks, efficiently balancing controller workloads, and reducing the duration of the balancing process in 5G networks. [ABSTRACT FROM AUTHOR]

As financial institutions navigate an increasingly complex cyber threat landscape and regulatory ecosystem, there is a pressing need for a robust and adaptive security architecture. This paper introduces a comprehensive, Zero Trust model-based framework specifically tailored for the finance industry. It encompasses identity and access management (IAM), data protection, and device and network security and introduces trust through blockchain technology. This study provides a literature review of existing Zero Trust paradigms and contrasts them with cybersecurity solutions currently relevant to financial settings. The research adopts a mixed methods approach, combining extensive qualitative analysis through a literature review and assessment of security assumptions, threat modelling, and implementation strategies with quantitative evaluation using a prototype banking application for vulnerability scanning, security testing, and performance testing. The IAM component ensures robust authentication and authorisation processes, while device and network security measures protect against both internal and external threats. Data protection mechanisms maintain the confidentiality and integrity of sensitive information. Additionally, the blockchain-based trust component serves as an innovative layer to enhance security measures, offering both tamper-proof verification and increased integrity. Through analysis of potential threats and experimental evaluation of the Zero Trust model's performance, the proposed framework offers financial institutions a comprehensive security architecture capable of effectively mitigating cyber threats and fostering enhanced consumer trust. [ABSTRACT FROM AUTHOR]

The world has seen exponential growth in deploying Internet of Things (IoT) devices. In recent years, connected IoT devices have surpassed the number of connected non-IoT devices. The number of IoT devices continues to grow and they are becoming a critical component of the national infrastructure. IoT devices' characteristics and inherent limitations make them attractive targets for hackers and cyber criminals. Botnet attack is one of the serious threats on the Internet today. This article proposes pattern-based feature selection methods as part of a machine learning (ML)-based botnet detection system. Specifically, two methods are proposed: the first is based on the most dominant pattern feature values and the second is based on maximal frequent itemset mining. The proposed feature selection method uses Gini impurity and an unsupervised clustering method to select the most influential features automatically. The evaluation results show that the proposed methods have improved the performance of the detection system. The developed system has a true positive rate of 100% and a false positive rate of 0% for best performing models. In addition, the proposed methods reduce the computational cost of the system as evidenced by the detection speed of the system. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) platforms are responsible for overall data processing in the IoT System. This ranges from analytics and big data processing to gathering all sensor data over time to analyze and produce long‐term trends. However, this comes with prohibitively high demand for resources such as memory, computing power and bandwidth, which the highly resource constrained IoT devices lack to send data to the platforms to achieve efficient operations. This results in poor availability and risk of data loss due to single point of failure should the cloud platforms suffer attacks. The integrity of the data can also be compromised by an insider, such as a malicious system administrator, without leaving traces of their actions. To address these issues, we propose in this work an edge‐based blockchain enabled anomaly detection technique to prevent insider attacks in IoT. The technique first employs the power of edge computing to reduce the latency and bandwidth requirements by taking processing closer to the IoT nodes, hence improving availability, and avoiding single point of failure. It then leverages some aspect of sequence‐based anomaly detection, while integrating distributed edge with blockchain that offers smart contracts to perform detection and correction of abnormalities in incoming sensor data. Evaluation of our technique using real IoT system datasets showed that the technique remarkably achieved the intended purpose, while ensuring integrity and availability of the data which is critical to IoT success. [ABSTRACT FROM AUTHOR]

Efficient and adaptive buffer management will play crucial role in the future integrated mobile network (e.g., 4th Generation). One of the main features of the future mobile networks will be their capability of providing different levels of quality of service to the different types of services. Some of these services will be delay and delay variation sensitive such as real-time voice and video. Others will be loss sensitive such as data transfer. But the most challenging will be the sort of multimedia traffic that owns both types of sensitivity. Another source of difficulty stems from the heterogeneous wireless air interface technologies that will act as border nodes to the network providing different traffic streams with different data rates and QoS requirements (e.g, WLAN provides data rates of 11Mbbps while UMTS can only provide 384Kbps). Although improved call admission control algorithms on the border of the network will provide a sort of protection from extensive traffic over the network, however, they will only operate on mean traffic volumes and will not cope with the variability of actual instantaneous traffic entering the network. Furthermore, as the core part of the mobile network will be packet switched based on IP, the QoS can only be maintained through efficient buffer management at the border and inside the network. This paper proposes and analytically evaluates a buffer management scheme that is based on multi-level priority and Partial Buffer Sharing (PBS) policy for all buffers at the border and inside the wireless network. The analytical model is based on the G/G/1/N censored queue with single server and R ( R2) priority classes under the Head of Line (HOL) service rule for the PBS scheme. The traffic is modelled using the Generalised Exponential distribution. The paper presents an analytical solution based on the approximation using the Maximum Entropy (ME) principle. The numerical results show the capability of the space prioritised buffer management scheme to provide higher QoS for the higher priority service classes [ABSTRACT FROM AUTHOR]

Thus cloud, IoT, and big data can be used together to offer new and exciting services in the aforementioned areas. Çiçek and Gören8 proposed a vision-based automated mobile cloud parking management solution, which can find road side parking spots in any street. CONCLUSION The papers presented in this special issue are related to the emerging topics in cloud computing, IoT, and big data. Cloud, Internet of Things (IoT), and big data have not only become the fundamental technologies and platforms in computing and network communication, but they have greater potential in a wide range of other areas such as business, finance, transportation, healthcare, social media, and engineering, among others. [Extracted from the article]

The understanding of cyber threats to a network is challenging yet rewarding as it allows an organisation to prevent a potential attack. Numerous efforts have been made to predict cyber threat before they occur. To build a threat intelligence framework, an organisation must understand attack data collected from the network events and analyse them to identify the cyber attack artefacts such as IP address, domain name, tools and techniques, username and password, and geographic location of the attacker, which could be used to understand the nature of attack to a system or network. However, it is very difficult or dangerous to collect and analyse live data from a production system. Honeypot technology is well known for mimicking the real system while collecting actual data that can be in near real time in order to monitor the activities on the network. This paper proposes a threat intelligence approach analysing attack data collected using cloud-based web service in order to support the active threat intelligence. [ABSTRACT FROM AUTHOR]

Failure is an increasingly important issue in high performance computing and cloud systems. As large-scale systems continue to grow in scale and complexity, mitigating the impact of failure and providing accurate predictions with sufficient lead time remains a challenging research problem. Traditional existing fault-tolerance strategies such as regular check-pointing and replication are not adequate because of the emerging complexities of high performance computing systems. This necessitates the importance of having an effective as well as proactive failure management approach in place aimed at minimizing the effect of failure within the system. With the advent of machine learning techniques, the ability to learn from past information to predict future pattern of behaviours makes it possible to predict potential system failure more accurately. Thus, in this paper, we explore the predictive abilities of machine learning by applying a number of algorithms to improve the accuracy of failure prediction. We have developed a failure prediction model using time series and machine learning, and performed comparison based tests on the prediction accuracy. The primary algorithms we considered are the support vector machine (SVM), random forest (RF), k-nearest neighbors (KNN), classification and regression trees (CART) and linear discriminant analysis (LDA). Experimental results indicates that the average prediction accuracy of our model using SVM when predicting failure is 90% accurate and effective compared to other algorithms. This finding implies that our method can effectively predict all possible future system and application failures within the system. [ABSTRACT FROM AUTHOR]

Cloud fault tolerance is an important issue in cloud computing platforms and applications. In the event of an unexpected system failure or malfunction, a robust fault-tolerant design may allow the cloud to continue functioning correctly possibly at a reduced level instead of failing completely. To ensure high availability of critical cloud services, the application execution, and hardware performance, various fault-tolerant techniques exist for building self-autonomous cloud systems. In comparison with current approaches, this paper proposes a more robust and reliable architecture using optimal checkpointing strategy to ensure high system availability and reduced system task service finish time. Using pass rates and virtualized mechanisms, the proposed smart failover strategy (SFS) scheme uses components such as cloud fault manager, cloud controller, cloud load balancer, and a selection mechanism, providing fault tolerance via redundancy, optimized selection, and checkpointing. In our approach, the cloud fault manager repairs faults generated before the task time deadline is reached, blocking unrecoverable faulty nodes as well as their virtual nodes. This scheme is also able to remove temporary software faults from recoverable faulty nodes, thereby making them available for future request. We argue that the proposed SFS algorithm makes the system highly fault tolerant by considering forward and backward recovery using diverse software tools. Compared with existing approaches, preliminary experiment of the SFS algorithm indicates an increase in pass rates and a consequent decrease in failure rates, showing an overall good performance in task allocations. We present these results using experimental validation tools with comparison with other techniques, laying a foundation for a fully fault-tolerant infrastructure as a service cloud environment. Copyright © 2017 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The reliability of delivering packets through multi-hop intermediate nodes is a significant issue in the mobile ad hoc networks (MANETs). The distributed mobile nodes establish connections to form the MANET, which may include selfish and misbehaving nodes. Recommendation based trust management has been proposed in the literature as a mechanism to filter out the misbehaving nodes while searching for a packet delivery route. However, building a trust model that adopts recommendations by other nodes in the network is a challenging problem due to the risk of dishonest recommendations like bad-mouthing, ballot-stuffing, and collusion. This paper investigates the problems related to attacks posed by misbehaving nodes while propagating recommendations in the existing trust models. We propose a recommendation based trust model with a defence scheme, which utilises clustering technique to dynamically filter out attacks related to dishonest recommendations between certain time based on number of interactions, compatibility of information and closeness between the nodes. The model is empirically tested under several mobile and disconnected topologies in which nodes experience changes in their neighbourhood leading to frequent route changes. The empirical analysis demonstrates robustness and accuracy of the trust model in a dynamic MANET environment. [ABSTRACT FROM AUTHOR]

In recent years, the Internet has enabled access to widespread remote services in the distributed computing environment; however, integrity of data transmission in the distributed computing platform is hindered by a number of security issues. For instance, the botnet phenomenon is a prominent threat to Internet security, including the threat of malicious codes. The botnet phenomenon supports a wide range of criminal activities, including distributed denial of service (DDoS) attacks, click fraud, phishing, malware distribution, spam emails, and building machines for illegitimate exchange of information/materials. Therefore, it is imperative to design and develop a robust mechanism for improving the botnet detection, analysis, and removal process. Currently, botnet detection techniques have been reviewed in different ways; however, such studies are limited in scope and lack discussions on the latest botnet detection techniques. This paper presents a comprehensive review of the latest state-of-the-art techniques for botnet detection and figures out the trends of previous and current research. It provides a thematic taxonomy for the classification of botnet detection techniques and highlights the implications and critical aspects by qualitatively analyzing such techniques. Related to our comprehensive review, we highlight future directions for improving the schemes that broadly span the entire botnet detection research field and identify the persistent and prominent research challenges that remain open. [ABSTRACT FROM AUTHOR]

This paper investigates into the performance and load management of web servers that are deployed in commercial websites. Such websites offer various services such as flight/hotel booking, online banking, stock trading, and product purchases among others. Customers are increasingly relying on these round-the-clock services which are easier and (generally) cheaper to order. However, such an increasing number of customers' requests makes a greater demand on the web servers. This leads to web servers' overload and the consequential provisioning of inadequate level of service. This paper addresses these issues and proposes an admission control scheme which is based on the class-based priority scheme that classifies customer's requests into different classes. The proposed scheme is formally specified using $$\Pi $$ -calculus and is implemented as a Java-based prototype system. The prototype system is used to simulate the behaviour of commercial website servers and to evaluate their performance in terms of response time, throughput, arrival rate, and the percentage of dropped requests. Experimental results demonstrate that the proposed scheme significantly improves the performance of high priority requests but without causing adverse effects on low priority requests. [ABSTRACT FROM AUTHOR]

A wireless sensor network is a network of large numbers of sensor nodes, where each sensor node is a tiny device that is equipped with a processing, sensing subsystem and a communication subsystem. The critical issue in wireless sensor networks is how to gather sensed data in an energy-efficient way, so that the network lifetime can be extended. The design of protocols for such wireless sensor networks has to be energy-aware in order to extend the lifetime of the network because it is difficult to recharge sensor node batteries. We propose a protocol to form clusters, select cluster heads, select cluster senders and determine appropriate routings in order to reduce overall energy consumption and enhance the network lifetime. Our clustering protocol is called an Efficient Cluster-Based Communication Protocol (ECOMP) for Wireless Sensor Networks. In ECOMP, each sensor node consumes a small amount of transmitting energy in order to reach the neighbour sensor node in the bidirectional ring, and the cluster heads do not need to receive any sensed data from member nodes. The simulation results show that ECOMP significantly minimises energy consumption of sensor nodes and extends the network lifetime, compared with existing clustering protocol. [ABSTRACT FROM AUTHOR]

As part of a defense-in-depth security solution for domain-controlled enterprise networks, a proposed self-healing system architecture is designed to increase resiliency against botnets with minimal disruption to network services. [ABSTRACT FROM AUTHOR]

Rapid advances in software systems, wireless networks, and embedded devices have led to the development of a pervasive and mobile cyberspace that provides an infrastructure for anywhere/anytime service provisioning in different domains such as engineering, commerce, education, and entertainment. This style of service provisioning enables users to freely move between geographical areas and to continuously access information and conduct online transactions. However, such a high mobility may cause performance and reliability problems during the execution of transactions. For example, the unavailability of sufficient bandwidth can result in failure of transactions when users move from one area (cell) to another. We present a context-aware transaction model that dynamically adapts to the users' needs and execution environments. Accordingly, we develop a new mobility management scheme that ensures seamless connectivity and reliable execution of context-aware transactions during mobility of users. The proposed scheme is designed and developed using a combination of different queuing models. We conduct various experiments in order to show that the proposed scheme optimizes the mobility management process and increases the throughput of context-aware transactions. [ABSTRACT FROM PUBLISHER]

sensor node is a tiny device that includes a sensing subsystem, a processing subsystem and a wireless communication subsystem. The sensor nodes have limited resources in terms of memory, energy and computational resources. Clustering has been proposed by researches to group a number of nodes to form a cluster that managed by cluster head. However, most existing clustering protocols consume large amount of energy due to cluster formation overhead and cluster head responsibilities. We proposed an energy efficient clustering protocol (EECPL) to enhance lifetime of wireless sensor networks. The main goal of EECPL is to distribute the energy load among all sensor nodes to minimize the energy consumption and maximize the network lifetime of wireless sensor networks. EECPL organizes sensor nodes into clusters and uses ring topology to send data packets so that each sensor node receives data from a previous neighbor and transmits data to a next neighbor. Upon receiving the aggregated data from previous neighbors, cluster senders transmit the aggregated data to the base station. EECPL is shown to achieve significant energy savings, balance the energy consumption among sensor nodes and reduce communication overhead. [ABSTRACT FROM AUTHOR]

Broadcasting is an essential and effective data propagation mechanism, with several of important applications such as route discovery, address resolution, as well as many other network services. As data broadcasting has many advantages, also causing a lot of contention, collision, and congestion, which induces what is known as "broadcast storm problems". Broadcasting has traditionally been based on the flooding protocol, which simply overflows the network with high number of rebroadcast messages until the messages reach to all network nodes. A good probabilistic broadcasting protocol can achieve higher saved rebroadcast, low collisions and less number of relays. In this paper, we propose a dynamic probabilistic approach that dynamically fine-tunes the rebroadcasting probability according to the number of neighbour's nodes distributed in the ad hoc network for routing request packets (RREQs). The performance of the proposed approach is investigated and compared with the simple AODVand fixed probabilistic schemes using the GloMoSim network simulator under different mobility models. The performance results reveal that the improved approach is able to achieve higher saved rebroadcast and low collision as well as low number of relays than the fixed probabilistic scheme and simple AODV. [ABSTRACT FROM AUTHOR]

Service scheduling is one of the crucial issues in E-commerce environment. E-commerce web servers often get overloaded as they have to deal with a large number of customers’ requests—for example, browse, search, and pay, in order to make purchases or to get product information from E-commerce web sites. In this paper, we propose a new approach in order to effectively handle high traffic load and to improve web server’s performance. Our solution is to exploit networking techniques and to classify customers’ requests into different classes such that some requests are prioritised over others. We contend that such classification is financially beneficial to E-commerce services as in these services some requests are more valuable than others. For instance, the processing of “browse” request should get less priority than “payment” request as the latter is considered to be more valuable to the service provider. Our approach analyses the arrival process of distinct requests and employs a priority scheduling service at the network nodes that gives preferential treatment to high priority requests. The proposed approach is tested through various experiments which show significant decrease in the response time of high priority requests. This also reduces the probability of dropping high priority requests by a web server and thus enabling service providers to generate more revenue. [ABSTRACT FROM AUTHOR]

Traffic congestion degrades not only the user-perceived Quality-of-Service (QoS), such as leading to high packet loss rates, low throughput, and increased delays, but also causes excessive energy consumption in energy-sensitive systems (e.g., wireless sensor networks). A simple way to detect congestion is to monitor and measure queue length in network nodes or routers. This paper develops an analytical performance model for a finite capacity queueing system with an enhanced Random Early Detection (RED) congestion control scheme based on the instantaneous queue length in the presence of differentiated classes of bursty traffic. The aggregate traffic is captured by the superposition of 2-state Markov Modulated Poisson Processes (MMPP). The individual threshold is assigned to each traffic class in order to differentially control traffic injection rate. The accuracy of this model is verified by comparing the analytical results against those obtained from simulation experiments. The model is adopted to investigate the effects of traffic burstiness and system capacity on the performance of the congestion control scheme. [ABSTRACT FROM AUTHOR]

The article offers an introduction to a series of articles on performance analysis and enhancements of wireless networks including "DS/CDMA throughput of a multi-hop sensor network in a Rayleigh fading underwater acoustic channel," "Effect of redundancy on the mean time to failure of wireless sensor networks," "EZRP: an enhanced zone-based routing protocol for ad-hoc networks," "Augmenting Reed–Solomon coding with retransmission for error recovery in 3G video broadcasts," "SCTP performance evaluation over heterogeneous networks," "Distributed communication model in hierarchical Infostation systems," "QoS-aware mobile service transactions in a wireless environment," "Modeling and evaluation of congestion control for different classes of network traffic," "Mobility management in hybrid ad-hoc networks and the Internet environment," and "Towards building a conflict-free mobile distributed file system."

Traffic congestion has become a crucial problem in wireless networks due to its detrimental effects on the end-to-end Quality-of-Service (QoS) of mobile users. This issue has posed various challenges to developing effective congestion control mechanisms and devising new performance evaluation tools. Various congestion control schemes have been proposed to support differentiated QoS. Among these schemes, Active Queue Management (AQM) has been recognized as an effective mechanism to manage downlink buffers in wireless networks. Most existing studies on AQM are based on the use of averaged queue length and the related performance analysis is carried out via software simulation. This paper contributes to the development of an analytical performance model for a finite capacity queuing system subject to AQM and multiple traffic classes based on the instantaneous queue length. Expressions for the aggregated and marginal performance metrics including the mean queuing length, packet loss probability, mean waiting time, system throughput, response time, and utilization are analytically derived. The validity of this new model is demonstrated by comparing the analytical results against those obtained from simulation experiments. The analytical model is then used to investigate the effects of varying thresholds on the aggregated and marginal performance metrics under different combinations of arriving rates for multiple traffic classes. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Credit-based flow control scheme that can be used to support both end-to-end and link-level flow control is becoming increasingly popular in high speed system area networks (SAN), e.g. InfiniBand networks where multiple processor nodes and I/O devices are interconnected using switched point-to-point links. By virtue of such a scheme, the downstream node sends credits to the upstream node indicating the availability of buffer spaces. Upon receiving credits, the upstream node injects packets into the networks. Performance analysis of credit-based flow control scheme plays an important role for the design and optimization of InfiniBand interconnection networks which have been widely used in many high-performance cluster, Grid and P2P computing systems. This study develops a new queueing network model for performance evaluation of credit-based flow control in InfiniBand networks. The performance metrics to be derived include the mean queue length, throughput and response time of the system. Simulation experiments have been used to validate the accuracy of the queueing network model. Results obtained from the analytical model have showed that this model can effectively evaluate the performance of credit-based flow control in InfiniBand networks. [ABSTRACT FROM AUTHOR]

Increasing number of sophisticated services provided by the current wireless communication systems have caused a significant transition from E-commerce to M-commerce. Enterprises have provided considerable new opportunities to promote their businesses accessible from small mobile devices such as personal digital assistant (PDA) or mobiles phones. These facilities are envisioned as the most convenient way of using M-commerce. Using such services from mobile phones or PDAs equipped with GSM/GPRS involve effective mobility management mechanisms in cellular networks—a popular architecture for wireless networks. Success of such systems will largely depend on the reliable connection for the roaming users. Consequently, Quality of Service (QoS) provision is one of the most challenging issues in the heterogeneous wireless network-based m-commerce systems. Such m-commerce systems enable users to roam between different wireless networks operators and geographical areas while providing interactive broadband services and seamless connectivity. This paper presents an analytical framework to model scarce channels in any cell for maximizing channel utilization and efficient handling of handover requests. Typical numerical experiments are presented to validate the analytical solution against simulation to study the effect of bursty traffic upon the performance of the cell in any cellular networks. [ABSTRACT FROM AUTHOR]

Many performance models for deterministic routing in multicomputer interconnection networks have been derived and analyzed under the assumption of the traditional Poisson stochastic arrival process, which is inherently unable to capture traffic self-similarity revealed by many real-world parallel applications. In an effort towards understanding the network performance under various traffic loads and different design alternatives, this paper presents an analytical model for dimension-ordered routing in k-ary n-cubes when subjected to self-similar traffic. As the service time, blocking probability and waiting time experienced by a message vary from a dimension to another, the design of such a model for dimension-ordered routing poses greater challenges. The developed analytical model is then used to investigate the efficiency of two different ways to organize virtual channels for deterministic routing and to evaluate the impact of self-similar traffic with various Hurst parameters on network performance. [ABSTRACT FROM AUTHOR]