text: A sensor network is a collection of wireless devices that are able to monitor physical or environmental conditions. These devices are expected to operate autonomously, be battery powered and have very limited computational capabilities. This makes the task of protecting a sensor network against misbehavior or possible malfunction a challenging problem. In this document we discuss performance of Artificial immune systems (AIS) when used as the mechanism for detecting misbehavior. We concentrate on performance of respective genes; genes are necessary to measure a network's performance from a sensor's viewpoint. We conclude that the choice of genes has a profound influence on the performance of the AIS. We identified a specific MAC layer based gene that showed to be especially useful for detection. We also discuss implementation details of AIS when used with sensor networks.
  @cite_9: This paper studies a simplified form of LISYS, an artificial immune system for network intrusion detection. The paper describes results based on a new, more controlled data set than that used for earlier studies. The paper also looks at which parameters appear most important for minimizing false positives, as well as the trade-offs and relationships among parameter settings.
  @cite_13: This paper investigates the role of negative selection in an artificial immune system (AIS) for network intrusion detection. The work focuses on the use of negative selection as a network traffic anomaly detector. The results of the negative selection algorithm experiments show a severe scaling problem for handling real network traffic data. The paper concludes by suggesting that the most appropriate use of negative selection in the AIS is as a filter for invalid detectors, not the generation of competent detectors.
summary: Ref. @cite_13 discusses a network intrusion system that aims at detecting misbehavior by capturing TCP packet headers. They report that their AIS is unsuitable for detecting anomalies in communication networks. This result is questioned in @cite_9 where it is stated that this is due to the choice of problem representation and due to the choice of matching threshold @math for @math -contiguous bits matching.