Degree-Based Clustering Algorithms for Wireless Ad Hoc Networks Under Attack

In this paper we investigate the behavior of degree-based clustering algorithms with respect to their stability and attack-resistance. Our attack scenario tries to bias the clustering head selection procedure by sending faulty degree claims. We propose a randomized variant of the highest degree algorithm which is proved, through experimental results, attack-resistant without imposing significant overhead to the clustering performance. In addition, we extend our proposal with a cooperative consistent clustering algorithm which integrates security into the clustering decision achieving attacker identification and classification

SAnoVs: Secure Anonymous Voting Scheme for clustered ad hoc networks

In this paper we propose a secure anonymous voting scheme (SAnoVS) for re-clustering in the ad-hoc network. SAnoVS extends our previous work of degree-based clustering algorithms by achieving anonymity and confidentiality of the voting procedure applied to select new cluster heads. The security of SAnoVS is based on the difficulty of computing discrete logarithms over elliptic curves, the intractability of inverting a one-way hash function and the fact that only neighboring nodes contribute to the generation of a shared secret. Furthermore, we achieve anonymity since our scheme does not require any identification information as we make use of a polynomial equation system combined with pseudo-random coordinates. The security analysis of our scheme is demonstrated with several attacks scenarios.examined with several attack scenarios and experimental results

A Fair and Secure Cluster Formation Process for Ad Hoc Networks

An efficient approach for organizing large ad hoc networks is to divide the nodes into multiple clusters and designate, for each cluster, a clusterhead which is responsible for holding intercluster control information. The role of a clusterhead entails rights and duties. On the one hand, it has a dominant position in front of the others because it manages the connectivity and has access to other node¿s sensitive information. But on the other hand, the clusterhead role also has some associated costs. Hence, in order to prevent malicious nodes from taking control of the group in a fraudulent way and avoid selfish attacks from suitable nodes, the clusterhead needs to be elected in a secure way. In this paper we present a novel solution that guarantees the clusterhead is elected in a cheat-proof manner

AMISEC: Leveraging Redundancy and Adaptability to Secure AmI Applications

Security in Ambient Intelligence (AmI) poses too many challenges due to the inherently insecure nature of wireless sensor nodes. However, there are two characteristics of these environments that can be used effectively to prevent, detect, and confine attacks: redundancy and continuous adaptation. In this article we propose a global strategy and a system architecture to cope with security issues in AmI applications at different levels. Unlike in previous approaches, we assume an individual wireless node is vulnerable. We present an agent-based architecture with supporting services that is proven to be adequate to detect and confine common attacks. Decisions at different levels are supported by a trust-based framework with good and bad reputation feedback while maintaining resistance to bad-mouthing attacks. We also propose a set of services that can be used to handle identification, authentication, and authorization in intelligent ambients. The resulting approach takes into account practical issues, such as resource limitation, bandwidth optimization, and scalability

Secure Clustering in DSN with Key Predistribution and WCDS

This paper proposes an efficient approach of secure clustering in distributed sensor networks. The clusters or groups in the network are formed based on offline rank assignment and predistribution of secret keys. Our approach uses the concept of weakly connected dominating set (WCDS) to reduce the number of cluster-heads in the network. The formation of clusters in the network is secured as the secret keys are distributed and used in an efficient way to resist the inclusion of any hostile entity in the clusters. Along with the description of our approach, we present an analysis and comparison of our approach with other schemes. We also mention the limitations of our approach considering the practical implementation of the sensor networks.Comment: 6 page

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial