Trustability for Resilient Internet of Things Services on 5G Multiple Access Edge Cloud Computing

Abstract

Billions of Internet of Things (IoT) devices and sensors are expected to be supported by fifth-generation (5G) wireless cellular networks. This highly connected structure is predicted to attract different and unseen types of attacks on devices, sensors, and networks that require advanced mitigation strategies and the active monitoring of the system components. Therefore, a paradigm shift is needed, from traditional prevention and detection approaches toward resilience. This study proposes a trust-based defense framework to ensure resilient IoT services on 5G multi-access edge computing (MEC) systems. This defense framework is based on the trustability metric, which is an extension of the concept of reliability and measures how much a system can be trusted to keep a given level of performance under a specific successful attack vector. Furthermore, trustability is used as a trade-off with system cost to measure the net utility of the system. Systems using multiple sensors with different levels of redundancy were tested, and the framework was shown to measure the trustability of the entire system. Furthermore, different types of attacks were simulated on an edge cloud with multiple nodes, and the trustability was compared to the capabilities of dynamic node addition for the redundancy and removal of untrusted nodes. Finally, the defense framework measured the net utility of the service, comparing the two types of edge clouds with and without the node deactivation capability. Overall, the proposed defense framework based on trustability ensures a satisfactory level of resilience for IoT on 5G MEC systems, which serves as a trade-off with an accepted cost of redundant resources under various attacks.

Artificial intelligence control for trust‐based detection of attackers in 5G social networks

“…Numerous studies have explored trust modeling and its application in diverse scenarios. For instance, a trust framework proposed by Reference 11, grounded in measurement theory, finds applicability across various domains, including crime detection, 12 social networks, 13,14 the food-energy sector, [15][16][17][18][19][20] healthcare, 21,22 edge computing, [23][24][25][26][27] quantum computing, 28 and beyond. Recent work 21,[29][30][31] has highlighted trust's utility as an acceptance criterion for artificial intelligence algorithms.…”

AI Control for Trust-based Detection of Attackers in 5G Social Networks

“…Numerous studies have explored trust modeling and its application in diverse scenarios. For instance, a trust framework proposed by 11 , grounded in measurement theory, finds applicability across various domains, including crime detection 12 , social networks 13,14 , the food-energy sector 15,16,17,18,19,20 , healthcare 21,22 , edge computing 23 , quantum computing 24 , and beyond. Recent work 21,25,26,27 has highlighted trust's utility as an acceptance criterion for artificial intelligence algorithms.…”