Analysis of Artificial Immune Systems with Network Intrusion Detection Implementation

In this paper, we conduct a detailed analysis of artificial immune system principles and propose a network intrusion detection system based on the negative selection algorithm. The implementation involves generating detector sets through randomized pattern creation and self-tolerance screening, where detectors that match normal patterns are eliminated. Additionally, we introduce an r-chunk matching rule for anomaly event identification, which operates by comparing fixed-length character blocks against behavioral signatures. The rule's implementation utilizes sliding window techniques to process sequential data streams efficiently. Through in-depth vulnerability analysis of the rule-set, including collision probability assessment and pattern coverage evaluation, we identify potential weaknesses in detection coverage. These findings enable systematic improvements to the system's security and accuracy. The research outcomes provide significant references and insights for cybersecurity development, with promising potential for broader applications in future studies.