In recent years mobile ad hoc networks (MANETs) have become a very popular research topic. By providing communication in the absence of a fixed infrastructure they are very attractive for many applications such as tactical and disaster recovery operations and virtual conferences. On the other hand, this flexibility introduces new security risks. Moreover, different characteristics of MANETs make conventional security systems ineffective and inefficient for this new environment. Intrusion detection, which is an indispensable part of a security system, presents also a particular challenge due to the dynamic nature of MANETs, the lack of central points, and their highly constrained nodes. In this paper, we propose to investigate the use of an artificial intelligence based learning technique to explore this difficult design space. The grammatical evolution technique inspired by natural evolution is explored to detect known attacks on MANETs such as DoS attacks and route disruption attacks. Intrusion detection programs are evolved for each attack and distributed to each node on the network. The performance of these programs is evaluated on different types of networks with different mobility and traffic patterns to show their effects on intrusion detection ability
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