Cats & Kittens

My Master's Thesis Abstract

Low cost availability of sensor nodes makes them an attractive choice for sensor networks and their applications. To keep the costs low, sensor nodes are generally unshielded. This unshielded nature of sensor-network nodes combined with their ease of deployment, makes them vulnerable because an adversary can capture these nodes, copy security information to make replicas and deploy the replicas in the network to render malicious attacks. Replication attacks can be extremely hazardous to a network if done in a strategic way. For any node replication detection protocol, the three most important design issues are memory usage, detection probability and energy consumption. Previous node replication detection schemes either incur large memory overhead or consume excessive energy, particularly in the central region of the network. This thesis presents a Memory Efficient Line-Selected Multicast (MELSeM) algorithm which uses efficient bloom filter data structure. We propose a novel distributed technique for detecting node replication attacks using MELSeM. MELSeM reduces the average memory overhead of the network by nearly 70% than the previous distributed schemes while achieving nearly same detection probability.