IEEE Transactions on Software Engineering - Special issue on computer security and privacy
Ariadne: a secure on-demand routing protocol for ad hoc networks
Proceedings of the 8th annual international conference on Mobile computing and networking
Self-Organized Public-Key Management for Mobile Ad Hoc Networks
IEEE Transactions on Mobile Computing
Intrusion Detection Using Mobile Agents in Wireless Ad Hoc Networks
KMN '02 Proceedings of the IEEE Workshop on Knowledge Media Networking
SEAD: Secure Efficient Distance Vector Routing for Mobile Wireless Ad Hoc Networks
WMCSA '02 Proceedings of the Fourth IEEE Workshop on Mobile Computing Systems and Applications
A cooperative intrusion detection system for ad hoc networks
Proceedings of the 1st ACM workshop on Security of ad hoc and sensor networks
A modular architecture for distributed IDS in MANET
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartIII
IEEE Network: The Magazine of Global Internetworking
Evolutionary computation techniques for intrusion detection in mobile ad hoc networks
Computer Networks: The International Journal of Computer and Telecommunications Networking
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The mobile ad hoc networks are particularly vulnerable to intrusion, as its features of open medium, dynamic changing topology, cooperative routing algorithms. The traditional way of protecting networks with firewalls and encryption software is no longer sufficient and effective for those features, because no matter how secure the mobile ad hoc networks, its is still possible the nodes are compromised and become malicious. In this paper, we propose a novel intrusion detection approach for mobile ad hoc networks by using finite state machine. We construct the finite state machine (FSM) by the way of manually abstracting the correct behaviours of the node according to the routing protocol of Dynamic Source Routing (DSR). The monitor nodes cooperatively monitor every node's behaviour by the FSM. Our approach can detect real-time attacks without signatures of intrusion or trained data. Finally, we evaluate the intrusion detection method through simulation experiments.