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an adversary s malicious attacks. Generally speaking, an intrusion can be defined as an act of a person or proxy attempting to break into or misuse your system in violation of an established policy. Very little research work dealing with the intrusion problem has been done for wireless networks. In this section, we shall describe the intrusion problem in general. We hope that researchers will pick up what has been done in related areas, and find efficient approaches on how to deal with this problem in an ad hoc network environment. 14.4.1 Current IDS Techniques Generally speaking, intrusion can be classified as: (i) misuse intrusions, i.e., well-defined attacks against known system vulnerabilities; and (ii) anomaly intrusions, i.e., activities based on deviation from normal system usage patterns. Intrusion detection systems (IDS) are one of the latest security tools in the battle against these attacks. As is well known, it is very difficult to determine exactly which activities provide the best indicators for the established (normal) usage patterns. Thus, researchers have turned to using expert systems or knowledge-based intrusion detection to search for activities known to be indicative of possible intrusive behavior [16]. The motivation behind this approach is to seek a proper behavior as opposed to a normal one. Knowledge-based intrusion detection schemes apply the knowledge they have accumulated about specific attacks and system vulnerabilities. Using this knowledge database, any action that is not explicitly recognized as an attack is considered acceptable. Otherwise, an alarm is triggered by the system. There are many different intrusion systems available in the marketplace. Expert systems are based on knowledge-based intrusion detection techniques. Each attack is identified by a set of rules. Rule-based languages [13] are used for modeling the knowledge that experts have accumulated about attacks/frauds. Information regarding some intruders has also been added to these systems. A major drawback of knowledge-based intrusion systems is the difficulty of gathering the information on the known attacks (which should be updated regularly) and developing a comprehensive set of rules that can be used to identify intrusive behaviors. Some systems use a combination of several approaches to cover both the normal and proper behavior schemes [17]. We refer to them as behavior-based intrusion detection. Their basic characteristic is that any action that does not match with a previously learned behavior triggers an alarm. The action is considered as intrusive. The main advantages of these systems are that they can exploit new and unforeseen attacks, and contribute to automatically discovering new attacks. However, their high false alarm rate is generally cited as a main drawback of these systems, due basically to the accuracy of the behavior information accumulated during the learning process.
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