Machine learning driven cyber resilience framework for mobile tactical networks with graph-based threat detection and adversarial security engineering in cyber physical systems

Abstract

Advanced mobile network connectivity is being fueled by the rapid evolution of 5G and the forthcoming 6G technologies. This however has exposed mobile networks to new cyber threats and security vulnerabilities. Consequently, cyber resilience, that is, the cope to prepare, identify, respond and recover from cyber-related incidents has become crucial. This paper focuses on a cyber resilience framework for mobile networks utilizing machine learning (ML) aiming at emerging threats. Machine Learning supervised, unsupervised and deep learning algorithms can perform anomaly detection, intrusion detection, prediction and automated threat response systems. Major ones like IDS and anomaly detection are discussed and analyzed with practical instances. The study examines and proposes federated learning, reinforcement learning and explainable AI (XAI) suffice in addressing issues of scarcity of data, time-sensitive processing, and emerging cyber threats. Integrating IoT, edge computers and 6G networks can also improve resilience. It is evident that there is great potential for cyber resilience through machine learning however it has been suggested that standardization, benchmarking and effective test frameworks are put in place