Intrusion Detection Techniques and Swarm Intelligence Cybersecurity Review

A computing and communications revolution at high speed has hastened the demand for effective security devices to protect networks from highly sophisticated cyberattacks.Intrusion Detection Systems (IDS) are an essential part of network traffic monitoring and network abuse detection.Traditional IDS techniques, however, such as signature-based and anomaly-based systems, experience severe limitations, including weak detection of novel attacks, high false positives, and high computational overhead.This survey provides a comprehensive overview of state-of-the-art hybrid machine learning (ML) methods with swarm intelligence (SI), a collection of metaheuristic optimization techniques inspired by collective behaviour in nature, for the enhancement of IDS.The examination is critical and covers hybrid models integrating supervised, unsupervised, and deep learning methods optimized using SI methods, such as crayfish optimization, firefly algorithm (FA), and social network search (SNS).Their key strengths and weaknesses and their applications in the real world are highlighted.Problems of computational complexity, scalability, and real-time use are also cited.The paper identifies critical areas for future research activity, such as improved feature selection methodology, real-time adaptability, distributed processing methodology, and large and diverse benchmark datasets.The survey highlights the immense scope for hybrid SI-based ML solutions to improve cybersecurity practice and research.