Adaptive Intrusion Detection for IoT Networks using Artificial Immune System Techniques: A Comparative Study

Abstract

The rapid proliferation of IoT devices has led to a significant increase in security vulnerabilities, rendering them susceptible to more sophisticated assaults. Conventional security methods often encounter difficulties in the changing surroundings and resource limitations of IoT, requiring flexible, low-resource alternatives. This research proposes the use of three distinct Artificial Immune System (AIS) methodologies to enhance the security of the Internet of Things (IoT). The concepts include clonal selection, negative selection, and risk theory. Each algorithm fulfills essential security requirements: Negative selection helps find new dangers, clonal selection finds things that aren't normal in real-time, and risk theory uses context-aware responses to reduce false positives. When tested on several IoT-specific datasets, the AIS framework had an average detection accuracy of 94%. It also had a 20% reduction in false-positive rates and made better use of resources than traditional machine learning models like SVM, RF, and KNN. The findings indicate that the framework is effective for resource-constrained IoT devices. They enhance IoT security by using adaptive, immune-inspired countermeasures tailored to the unique problems of IoT. The suggested approach guarantees that networked devices remain adequately protected against new threats. The conclusions indicated that integrating comprehensive security management into IoT frameworks might markedly diminish total risk, therefore facilitating safer and more dependable IoT applications.