Cooperative and smart attacks detection systems in 6G-enabled Internet of Things

The Sixth Generation (6G) of mobile networks offers the promise of a global interconnected system, serving a large set of applications across multiple fields such as satellite, air, ground, and underwater networks. It will evolve towards a unified network compute fabric that facilitates convergence across ecosystems, fostering design and innovation of new Internet of Things (IoT) applications and services, further leading to an exponential growth of IoT use cases in the post-6G era. This profound evolution will also impact the threat landscape, adding new threat actors, and leading to a new set of cyber security challenges. This paper reviews 6G applications and analyzes their security challenges and existing solutions, covering both the network, application and data layers. It introduces a new concept to security monitoring and attack detection in 6Genabled IoT systems, leveraging on hierarchical and collaborative approaches, while also satisfying the main 6G's Key Performance Indicators (KPIs) such as trustworthiness, latency, connectivity, data rate and energy consumption. The proposed solution implements a multi-level Federated Learning (FL) approach between IoT devices and edge computing applications. As compared to current centralized security monitoring and detection solutions, it conciliates better between the attack detection accuracy and the network overhead for implementing this model. We demonstrate the use of the proposed solution through an example scenario involving an Internet of Vehicles that communicate over a 6G network.