A survey: contribution of ML & DL to the detection & prevention of botnet attacks

Machine Learning (ML) and Deep Learning (DL) are transforming the detection and prevention of botnets, significant threats in cybersecurity. In this survey, we highlight the shift from traditional detection methods to advanced ML and DL techniques. We demonstrate their effectiveness through case studies involving classification algorithms, clustering techniques, and neural networks. We also explore innovative strategies like federated learning and meta-learning models that enhance proactive defenses, including predictive analytics, real-time systems, and automated responses. Our paper discusses challenges such as data privacy, model overfitting, and the need for adaptability to sophisticated botnet structures. We emphasize the importance of ongoing research and collaboration across disciplines to keep pace with fast-evolving cyber threats, offering insights for developing intelligent cybersecurity defenses. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.