A Scalable Method for Detecting Multi-Vector Attacks on Compromised IoT Devices Using Machine Learning Algorithms

The paper presents a new scalable method for detecting multi-vector attacks on compromised Internet of Things (IoT) devices using machine learning algorithms. A detailed analysis of existing attack detection methods is conducted, including signature-based methods, anomaly-based methods, supervised and unsupervised machine learning methods, as well as deep learning methods such as convolutional and recurrent neural networks. A new method combining deep learning and federated learning is developed, which improves the accuracy, scalability, and security of the system in the context of limited IoT device resources. The results of an experimental study confirming the superiority of the proposed method over existing solutions are presented.