Intrusion Detection systems using Real-Valued Negative Selection Algorithm with Optimized Detectors

Intrusion detection system (IDS) is one of the key security mechanisms to preserve integrity against attacks in networks. Immunity-base intrusion detection is a well-known approach to develop IDS in WSN. The Negative Selection Algorithm (NSA) is one of the immunity-base methods for classification of network attacks. The selection of the proper radius for self-samples is a challenging problem in NSA. These radiuses are used for training and constructing detectors. The incorrect selection of a radius can cause the problem of boundaries invasion and the overlap between the samples, which increase the false detection rate. To solve this problem, self-samples must be optimized before being used in the detection phase. In this paper, we propose a variable radius for self-sampling based on affinity density to reduce the false detection. The NSL-KDD data set is used to evaluate the proposed method. The results indicate that the proposed method can reduce the false detection rate and along with increasing true positive rate.