# A Distributed Trustable Framework for AI-Aided Anomaly Detection +
## 1 Introduction  
The transition towards sixth-generation (6G) networks has put computing and communications at the focus of research activities related to novel extremely low-latency/highthroughput and error-free applications and services [1; 2]. In this context, the increased number of parameters and devices in 6G networks highly complicates the analysis and optimization of the security and quality-of-service (QoS) trade-off. However, studying these types of relationships is critical to prevent potential points of failure and to enhance the use of network resources, increasing network performance and resilience against diverse attack types and service requirements. Sixth-generation networks are envisioned to radically change current network management strategies, relying on increased disaggregation and software-based architecture paradigms [3]. In this sense, the forthcoming 3 GPP Release 20 is expected to bridge advanced fifth-generation (5G) capabilities and lay the foundation for 6G by covering topics such as sustainability, artificial intelligence (AI), and security [4]. A key technology that will be integrated into 6G networks is AI/machine learning (AI/ML)-aided optimization. AI/ML-based techniques will handle resource management, aiming to optimize the use of radio resources and guarantee sustainable network operation [5]. At the same time, AI/ML is expected to play a prominent role in network security, including cyberattack detection and prevention, network traffic pattern analysis, protection of sensitive information, identification of suspicious activities, and automated response and recovery to security incidents [6]. Thus, to ensure that the 6G vision will materialize, new security, privacy and resiliency solutions must be developed and adapted to 6G topologies. The study in [7] focused on the analysis of different potential threats due to the usage of open-source tools and frameworks for 6G network deployment, including adversarial ML, fine-grained privacy attacks, and threats due to virtualization and containerization, and the impact of quantum computing on cryptographic algorithms. At the same time, mitigation strategies were proposed, including a zero-trust architecture (ZTA) and an automated management system for open-source security. In the ZTA case, it is assumed that an attacker might exist within the network, and so, various network entities must mutually and securely authenticate with other similar entities, using, for example, a public key infrastructure (PKI). In the case of the automated management system, all open-source information is recorded to avoid tampering attempts. Furthermore, the disaggregated, virtualized, and multi-vendor 6G infrastructures that will be developed pose significant hurdles when designing security and resilience solutions for heterogeneous, complex, and highly flexible infrastructures. Moreover, managing network and computing resources in such complex environments involves data gathering from diverse endpoints, which must be promptly processed [8]. So, significant challenges emerge for data collection and classification, as well as network optimization, to support services with increased bandwidth and latency requirements. Unfortunately, conventional optimization techniques may result in non-convex problems, prohibiting the extraction of optimal solutions in terms of computation complexity and latency. Some methods in the literature address this issue by dividing the original non-convex problem into a series of convex subproblems that are solved independently, but still, calculation errors can be inevitably introduced [9]. Still, even in this case, calculation errors can inevitably be introduced, leading to the repetition of calculations for each network segment. Another important aspect of the 6G era is the ever-increasing integration of AI/ML to support tasks such as resource management and attack detection and mitigation.