Application of Nature-Inspired Algorithms for Malicious Code Detection in Next Generation Programs: A Comprehensive Study

Abstract

The increasing sophistication of malicious code poses a significant threat to next-generation software environments, including cloud computing and the internet of things. This review paper provides a comprehensive analysis of the application of Nature-Inspired Algorithms (NIAs) in detecting and mitigating these threats. We examine the use of genetic algorithms, ant colony optimization, and particle swarm optimization for tasks such as feature selection, anomaly detection, and malware classification. The paper also explores hybrid NIA approaches and their integration with machine learning to enhance detection accuracy. We highlight the challenges and opportunities associated with applying NIAs in resource-constrained environments like IoT and discuss future trends, including the development of emerging NIAs and the incorporation of explainable AI. The review concludes that NIAs offer a promising avenue for developing adaptive and intelligent security systems capable of defending against evolving cyber threats, but that continued research is needed.