Botnet Detection: A Review of Machine Learning and AI Strategies

Botnets, networks of infected devices controlled by malicious actors, pose a significant threat to cybersecurity. The sophistication of botnet attacks has escalated, making their detection increasingly challenging. This review paper delves into the realm of machine learning (ML) and artificial intelligence (AI) strategies for botnet detection. It presents an analysis of the evolution of botnets and the corresponding development in detection techniques. The paper categorizes various ML and AI methodologies, examining their effectiveness, adaptability, and efficiency in identifying botnet activities. Key strategies include supervised and unsupervised learning, deep learning, and reinforcement learning, each tailored to specific aspects of botnet behavior. The review also highlights the challenges and limitations of current approaches, such as the need for extensive datasets, the dynamic nature of botnets, and the trade-off between accuracy and computational efficiency. The paper concludes with future research directions, emphasizing the integration of AI with other cybersecurity measures to enhance the robustness and resilience of botnet detection systems. This comprehensive overview aims to provide insights into the current state of botnet detection and encourage further advancements in this critical field of cybersecurity.