6G networks will take the digital services offered by 5G to a whole new level with considerably higher bit-rates, lower latency, and ultra reliability. However, the security of these systems is crucial to fulfill the promise of 6G. A critical element of this requirement is the efficient and pervasive security for the protection of 6G infrastructure and services. In this paper, we propose Moving Target Defense (MTD) as a key proactive defense element and elaborate on how it can be integrated into Beyond 5G systems. We also present the relevant research challenges and future research directions including the standardization perspective.

With the growing number of IoT (Internet of Things) devices and their particular characteristics compared to traditional systems, incumbent security mechanisms need to be advanced for secure and resilient IoT operation in current ICT systems. One particular standard, which tries to improve IoT security in that regard, is the Manufacturer Usage Description (MUD) by IETF. In this paper, as our main focus is to highlight the security gains of using MUD, we first discuss the critical threats to IoT devices based on available research. In the second step, we analyze the MUD technology to delineate where MUD is beneficial (or not) to address these security issues.

In this work, we present Graph Based Liability Analysis Framework (GRALAF) for root cause analysis (RCA) of the microservices. In this Proof-of-Concept (PoC) tool, we keep track of the performance metrics of microservices, such as service response time and CPU level values, to detect anomalies. By injecting faults in the services, we construct a Causal Bayesian Network (CBN) which represents the relation between service faults and metrics. The constructed CBN is used to predict the fault probability of services under given metrics which are assigned discrete values according to their anomaly states.

This work presents a Moving Target Defense (MTD) framework for the protection of network slices and virtual resources in a telco cloud environment. The preliminary implementation provides a closed-loop security management of services with proactive MTD operations to reduce the success probability of attacks, and reactive MTD operations, empowered by a tampering detection and a traffic-based anomaly detection system. MTD strategies are adaptive and optimized with deep reinforcement learning (deep-RL) for balancing costs, security, and availability goals defined in a Multi-Objective Markov Decision Process (MOMDP).