INTRUSION DETECTION AND PREVENTION THROUGH ARTIFICIAL IMMUNE SHIELDING

Abstract

Cybersecurity threats continue to evolve, necessitating robust, adaptive mechanisms to safeguard digital infrastructure. Intrusion Detection Systems (IDS) play a pivotal role in this context by monitoring and detecting malicious activities. Traditional IDS approaches, such as signature-based and anomaly-based detection, have limitations in adaptability and detection accuracy. Inspired by the Human Immune System (HIS), Artificial Immune Systems (AIS) offer a promising paradigm for designing adaptive, self-learning, and robust IDS. This paper presents a comprehensive review of immune-inspired IDS, focusing on models like Negative Selection Algorithm (NSA), Clonal Selection Algorithm (CSA), and Danger Theory. The paper also explores hybrid models integrating AIS with machine learning and evolutionary computation to enhance detection performance.