Search

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.

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.

Abstract: In this paper, we model database designs using queueing networks, giving visibility to the dynamic behaviour of the database design and allowing the database designer to experiment with different design decisions. Our approach is to abstract away the more detailed levels of the database system design by concentrating on the information that is available to the database designer at design time. It differs from other methods of database system performance evaluation in that the performance assessment is specifically targeted at the database design, not at the database system software architecture. We present a bottleneck evaluation of the Transaction Processing Performance Council TPC-C benchmark under different workload conditions and demonstrate how this affects database design decisions. [Copyright &y& Elsevier]

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.

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.

No abstract

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. Different cloud services offer platforms for IoT big data analytics and processing, albeit at prohibitively high requirements for resources like memory and bandwidth, to constrained IoT devices. Employing edge computing helps lower some of those requirements, improve data availability and implement anomaly detection, but shares similar security issues as the cloud. As we presented, blockchain technology offers the prospects of ensuring IoT data integrity and was integrated with the edge to safeguard the data, which proved effective against insider attacks targeted at the system.