Emerging Cybersecurity Challenges in Machine Learning-Enabled Smart Heterogeneous Networks

Abstract

The chapter tries to resolve this emerging problem of machine learning-based smart heterogeneous networks, and cybersecurity by creating a multi-layered security infrastructure which is scalable to detect and react to security attacks in real-time. It proposes a multi-modal deep learning (CNNLSTM), adaptive control by reinforcement learning, and privacy-sensitive controls, such as federated learning. Heterogeneous datasets, including the IoT traffic, intrusion detection benchmarks, and multi-step attack scenarios, which are synthetic, are trained and tested with the model. The accuracy, precision, recall, F1-score, AUC-ROC, and the computational efficiency metrics are used to measure the performance in dynamic and noisy settings. The suggested framework is also a better performer than the baseline models, where accuracy is 98.37 and has a better ability to resist multi-step and evolving cyber threats. Integration of reinforcement learning increases adaptability and multi-modal features fusion increases detection accuracy.