AIS-Driven Defense Mechanism for Network Intrusion Detection and Prevention

Abstract

As cyber threats grow increasingly sophisticated, there is a pressing need for defense mechanisms that can adjust and respond in real-time to protect digital environments. Intrusion Detection Systems (IDS) have become indispensable tools for identifying unauthorized or harmful activities within networks. However, traditional IDS methods—primarily signature-based and anomaly-based detection—often struggle to adapt quickly to novel attacks and may lack the accuracy needed in complex situations. Drawing inspiration from the dynamic nature of the human immune system, researchers have turned to Artificial Immune Systems (AIS) as a means to create IDS solutions that are both adaptive and capable of continual self-improvement. This paper provides an in-depth examination of immune-inspired intrusion detection models, placing special emphasis on the Negative Selection Algorithm (NSA), the Clonal Selection Algorithm (CSA), and the conceptual framework of Danger Theory. Additionally, the study delves into hybrid approaches that combine AIS with modern machine learning and evolutionary algorithms, aiming to push the boundaries of detection effectiveness.