Detecting malfunction in wireless sensor networks

The objective of this thesis is to detect malfunctioning sensors in wireless sensor networks. The ability to detect abnormality is critical to the security of any sensor network. However, the ability to detect a faulty wireless sensor is not trivial. Controlled repeatable experiments are difficult in wireless channels. A Redhat Linux. 7.0 Wireless Emulation Dynamic Switch software was used to solve this problem. Six nodes were configured with a node acting as a base station. The nodes were all part of a cell. This means that every node could communicate with all other nodes. A client-server program simulated the background traffic. Another program simulated a faulty node. A node was isolated as the faulty node while all other nodes were good. The experiment ran for several hours and the data was captured with tcpdump. The data was analyzed to conclusions based on a statistical comparison of good node versus bad node. The statistical delay on the good node was an average of 0.69 ms while the standard deviation was 0.49. This was much better than the delay on the bad node that was 0.225192 s with a standard deviation of 0.89. This huge difference in the delay indicated that the faulty node was detected statistically. A threshold value of I ms was chosen. The good node was within this value about 98% of the time. The bad node on the other hand was far out of this range and was definitely detected. The channel utilization data provided the same conclusion

ECM-GT: design of efficient computational modelling based on game theoretical approach towards enhancing the security solutions in MANET

Game Theory is a useful tool for exploring the issues concerning Mobile Ad-Hoc Network (or MANET) security. In MANETs, coordination among the portable nodes is more significant, which encompasses their vulnerability challenges to several security assaults and the inability to run securely, when storing its resources and manage secure routing between the nodes. Hence, it is imperative to design an efficient routing protocol to secure all nodes from unknown behaviors. In the current research study, the game-theory approach is utilized for analytical purpose and addresses the security problems in MANETs. The game-theoretic approach is mainly adopted to find the malicious activities in the networks. In the proposed work, a Bayesian-Signaling game model is proposed which analyses the behavior of both regular/normal and malicious nodes. The game model proposed also provides the finest actions of autonomous tactics for every node. A Bayesian-Equilibrium (BE) offers the best solution for games to resolve the incomplete information by joining strategies and players payoff which form an equilibrium. By exploiting the BE mechanism, the system can detect the behavior of regular as well as malicious nodes. Therefore, Efficient ComputationalModelling based on Game Theory or ECM-GT methodology will reduce the utility of malicious nodes and increase the utility of regular nodes. Also, it stimulates the best co-operation among the nodes by exploiting the reputation system. On comparing our results with the existing systems, it was found that the proposed algorithm performed better in the detection of malicious nodes, throughput, false positive rate and detection of attacks

Security in Distributed, Grid, Mobile, and Pervasive Computing

This book addresses the increasing demand to guarantee privacy, integrity, and availability of resources in networks and distributed systems. It first reviews security issues and challenges in content distribution networks, describes key agreement protocols based on the Diffie-Hellman key exchange and key management protocols for complex distributed systems like the Internet, and discusses securing design patterns for distributed systems. The next section focuses on security in mobile computing and wireless networks. After a section on grid computing security, the book presents an overview of security solutions for pervasive healthcare systems and surveys wireless sensor network security

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig