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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]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jss.2010.03.080 Byline: Mohamed Alnas, Irfan Awan, R.D.W. Holton Keywords: Mobile IPv6; Fast Handover; Mobility Management; Link-Layer Information; Handover Latency Abstract: Handover latency is the primary cause of packet loss resulting in performance degradation of standard Mobile IPv6. Mobile IPv6 with fast Handover enables a Mobile Node (MN) to quickly detect at the IP layer that it has moved to a new subnet by receiving link-related information from the link-layer; furthermore it gathers anticipative information about the new Access Point (AP) and the associated subnet prefix when the MN is still connected to the previous Corresponding Node (CN). This paper proposes an enhancement to Fast Mobile IPv6 handover (FMIPv6), based on link layer information, we also present performance evaluations in terms of the packet loss and handover latency using evaluation models. Author Affiliation: Mobile Computing, Networks and Security Research Group, School of Informatics, University of Bradford, Bradford, West Yorkshire, BD7 1DP, UK Article History: Received 9 November 2009; Revised 18 January 2010; Accepted 30 March 2010

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]

Byline: Lin Guan, Irfan U. Awan, Mike E. Woodward, Xingang Wang Explosive growth of applications for wireless networks and increasing number of users will cause significant increase in the traffic over radio channels. Consequently, uplink and downlink channels may suffer serious traffic congestion problems in the absence of effective congestion control schemes. One approach to enhance the performance of wireless services is to employ an AQM scheme for buffers at the downlink of wireless networks. This paper presents an analytical framework for the performance evaluation of AQM scheme based congestion control mechanism in WLANs. It also carries out a comprehensive performance comparison for various dropping strategies to see the impact of AQM scheme on WLANs using queue thresholds.

Byline: Shihang Yan, Geyong Min, Irfan Awan Admission and congestion control mechanisms are integral parts of the design and provision of Quality of Service (QoS) in interconnection networks. This study investigates the admission and congestion control mechanisms for InfiniBand networks in cluster-based computing systems. This paper presents an improved Link-by-Link (LL) admission control algorithm. In addition we propose an effective source response function which adopts the rate control to reduce congestion of multiple-class traffic in InfiniBand networks. Performance results obtained from simulation experiments and the comparison to existing schemes have demonstrated that the proposed admission control algorithm and the new resource response function are quite effective and suitable for InfiniBand networks.

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]