Reputation-based security protocol for MANETs in highly mobile disconnection-prone environments

This paper is concerned with fully distributed reputation-based mechanisms that improve security in MANETS. We introduce a number of optimisations to the current reputation schemes used in MANETs such as selective deviation tests and adaptive expiration timer that aim to deal with congestion and quick reputation convergence. We propose to use two different centrality measures for evaluation of the individual trust claims and resolving the aggregated ones. We design and build our prototype over AODV and test it in NS-2 in the presence of variable active blackhole attacks in highly mobile and sparse networks. Our results show that we achieve increased throughput while delay and jitter decrease and converge to AODV

Various Security Attacks and Trust Based Security Architecture for MANET

A Mobile Ad hoc Network is a group of wireless mobile computers in which nodes cooperate by forwarding packets to each other allowing them to communicate beyond direct wireless transmission range. Mobile Ad hoc Networks (MANET) has become an exciting and important technology in recent years because of the rapid proliferation of wireless devices. Security is an important issue for all kinds of networks including the Wireless Ad Hoc Networks. In this paper, we are presenting some of the reasons that have made MANETs more vulnerable to attacks than the traditional wired network. This paper also covers the security attributes and the various challenges to security design. This paper shreds light on some of the security attacks that exists in MANETs. This Paper also proposes Trust Based Security Architecture for MANET

Data-centric Misbehavior Detection in VANETs

Detecting misbehavior (such as transmissions of false information) in vehicular ad hoc networks (VANETs) is very important problem with wide range of implications including safety related and congestion avoidance applications. We discuss several limitations of existing misbehavior detection schemes (MDS) designed for VANETs. Most MDS are concerned with detection of malicious nodes. In most situations, vehicles would send wrong information because of selfish reasons of their owners, e.g. for gaining access to a particular lane. Because of this (\emph{rational behavior}), it is more important to detect false information than to identify misbehaving nodes. We introduce the concept of data-centric misbehavior detection and propose algorithms which detect false alert messages and misbehaving nodes by observing their actions after sending out the alert messages. With the data-centric MDS, each node can independently decide whether an information received is correct or false. The decision is based on the consistency of recent messages and new alert with reported and estimated vehicle positions. No voting or majority decisions is needed, making our MDS resilient to Sybil attacks. Instead of revoking all the secret credentials of misbehaving nodes, as done in most schemes, we impose fines on misbehaving nodes (administered by the certification authority), discouraging them to act selfishly. This reduces the computation and communication costs involved in revoking all the secret credentials of misbehaving nodes.Comment: 12 page