Can the human immune system inspire better computer security? This paper describes an artificial immune system (ARTIS) that mirrors the properties of natural immune systems, such as diversity, error tolerance, and dynamic learning, and self-monitoring. Designed as a general framework, ARTIS is applied to computer security in the form of a network intrusion detection system called LISYS. The LISYS system demonstrates effectiveness in detecting intrusions while maintaining low false positive rates. The system adapts and evolves over time by learning from new data. ARTIS learns as it analyzes network traffic and adjusts its detection mechanisms, improving its performance. LISYS, a concrete example of ARTIS, proves the viability of this approach in addressing computer security challenges. The ARTIS framework provides a foundation for building distributed adaptive systems that can be applied to various domains beyond computer security, offering a novel approach to problem-solving. This could have implications for the rise of artifical intelligence.
Published in Evolutionary Computation, this article on artificial immune systems aligns with the journal’s emphasis on computational methods inspired by biological evolution. It is relevant to the field of computer science and addresses evolving threats. This research aligns well with the journal's scope, providing a valuable perspective on adaptive systems for technological applications.