Preventing Model Poisoning Through Artificial Immune Networks

With the rise of large language models, artificial intelligence and other machine learning models are being incorporated into many aspects of industry. The threats targeting these models have also increased. One such threat is model poisoning, where malicious actors can poison models by manipulating the learning data, hampering the accuracy of said models. This paper explores the use of artificial immune networks (AINs) as a robust defense mechanism against such attacks. Drawing inspiration from biological immune systems, AINs are employed to detect and neutralize adversarial distortions effectively. An AIN is a bio-inspired computational model that uses ideas and concepts from the immune network theory, mainly the interactions of B-cells, their simulation and suppression of attacks, and the cloning and mutation process. By discussing how AINs can be used to prevent model poisoning as a valuable prevention mechanism, this paper contributes to the theoretical advancement in the field of cybersecurity but also offers practical implications for developing more secure AI systems.