A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

Future 6G communications are envisioned to enable a large catalogue of pioneering applications. These will range from networked Cyber-Physical Systems to edge computing devices, establishing real-time feedback control loops critical for managing Industry 5.0 deployments, digital agriculture systems, and essential infrastructures. The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised. While full automation will enhance industrial efficiency significantly, it concurrently introduces new cyber risks and vulnerabilities. In particular, unattended systems are highly susceptible to trust issues: malicious nodes and false information can be easily introduced into control loops. Additionally, Denialof-Service attacks can be executed by inundating the network with valueless noise. Current anomaly detection schemes require the entire transformation of the control software to integrate new steps and can only mitigate anomalies that conform to predefined mathematical models. Solutions based on an exhaustive data collection to detect anomalies are precise but extremely slow. Standard models, with their limited understanding of mobile networks, can achieve precision rates no higher than 75%. Therefore, more general and transversal protection mechanisms are needed to detect malicious behaviors transparently. This paper introduces a probabilistic trust model and control algorithm designed to address this gap. The model determines the probability of any node to be trustworthy. Communication channels are pruned for those nodes whose probability is below a given threshold. The trust control algorithm comprises three primary phases, which feed the model with three different probabilities, which are weighted and combined. Initially, anomalous nodes are identified using Gaussian mixture models and clustering technologies. Next, traffic patterns are studied using digital Bessel functions and the functional scalar product. Finally, the information coherence and content are analyzed. The noise content and abnormal information sequences are detected using a Volterra filter and a bank of Finite Impulse Response filters. An experimental validation based on simulation tools and environments was carried out. Results show the proposed solution can successfully detect up to 92% of malicious data injection attacks.