Biblio

Tactical Mobile Ad-hoc NETworks (T-MANETs) are mainly used in self-configuring automatic vehicles and robots (also called nodes) for the rescue and military operations. A high dynamic network architecture, nodes unreliability, nodes misbehavior as well as an open wireless medium make it very difficult to assume the nodes cooperation in the `ad-hoc network or comply with routing rules. The routing protocols in the T-MANET are unprotected and subsequently result in various kinds of nodes misbehavior's (such as selfishness and denial of service). This paper introduces a comprehensive analysis of the packet dropping attack includes three types of misbehavior conducted by insiders in the T-MANETs namely black hole, gray hole, and selfish behaviours. An insider threat model is appended to a state-of-the-art routing protocol (such as DSR) and analyze the effect of packet dropping attack on the performance evaluation of DSR in the T-MANET. This paper contributes to the existing knowledge in a way it allows further security research to understand the behaviours of the main threats in MANETs which depends on nods defection in the packet forwarding. The simulation of the packet dropping attack is conducted using the Network Simulator 2 (NS2). It has been found that the network throughput has dropped considerably for black and gray hole attacks whereas the selfish nodes delay the network flow. Moreover, the packet drop rate and energy consumption rate are higher for black and gray hole attacks.

The distinctive features of mobile ad hoc networks (MANETs), including dynamic topology and open wireless medium, may lead to MANETs suffering from many security vulnerabilities. In this paper, using recent advances in uncertain reasoning that originated from the artificial intelligence community, we propose a unified trust management scheme that enhances the security in MANETs. In the proposed trust management scheme, the trust model has two components: trust from direct observation and trust from indirect observation. With direct observation from an observer node, the trust value is derived using Bayesian inference, which is a type of uncertain reasoning when the full probability model can be defined. On the other hand, with indirect observation, which is also called secondhand information that is obtained from neighbor nodes of the observer node, the trust value is derived using the Dempster-Shafer theory (DST), which is another type of uncertain reasoning when the proposition of interest can be derived by an indirect method. By combining these two components in the trust model, we can obtain more accurate trust values of the observed nodes in MANETs. We then evaluate our scheme under the scenario of MANET routing. Extensive simulation results show the effectiveness of the proposed scheme. Specifically, throughput and packet delivery ratio (PDR) can be improved significantly with slightly increased average end-to-end delay and overhead of messages.