immune model based approach for network intrusion detection
Most current anomaly based intrusion detection systems (IDS) use methods which rely on labeled training data. Such kind of data is expensive to produce and it is difficult to train such systems. Also these methods have difficulty in detecting new types of attacks and high rate of false positives. This paper offers an approach based on immune model and immunocomputing which allows training IDS without labeled attacks and shows a good performance on detecting new intrusions. Using immune model as the basic architecture for intrusion detection, we use immunocomputing techniques to increase efficiency of this model. The proposed IDS works in three modes: training mode, monitoring mode and adaptation mode. The last one is responsible for adaptation of IDS to the network traffic, which improves the performance of intrusion detection.
intrusion detection systems immunocomputing artificial immune systems