Recent IoT proliferation has undeniably affected the way organizational activities and business procedures take place within several IoT domains such as smart manufacturing, food supply chain, intelligent transportation systems, medical care infrastructures etc. The number of the interconnected edge devices has dramatically increased, creating a huge volume of transferred data susceptible to leakage, modification or disruption, ultimately affecting the security level, robustness and QoS of the attacked IoT ecosystem. In an attempt to prevent or mitigate network abnormalities while accommodating the cohesiveness among the involved entities, modeling their interrelations and incorporating their structural, content and temporal attributes, graph-based anomaly detection solutions have been repeatedly adopted. In this article we propose, a multi-agent system, with each agent implementing a Graph Neural Network, in order to exploit the collaborative and cooperative nature of intelligent agents for anomaly detection. To this end, against the propagating nature of cyber-attacks such as the Distributed Denial-of-Service (DDoS), we propose a distributed detection scheme, which aims to monitor efficiently
Cyberattacks on the Internet of Things (IoT) can cause major economic and physical damage, and disrupt production lines, manufacturing processes, supply chains, impact the physical safety of vehicles, and damage the health of human beings. Thus we describe and evaluate a distributed and robust attack detection and mitigation system for network environments where communicating decision agents use Graph Neural Networks to provide attack alerts. We also present an attack mitigation system that uses a Reinforcement Learning driven Software Defined Network to process the alerts generated by the attack detection sysem, together with Quality of Service measurements, so as to reroute sensitive traffic away from compromised network paths using. Experimental results illustrate both the detection and rerouting scheme.
The Internet of Things (IoT) is growing rapidly controlling and connecting thousands of devices every day. Software Defined Networking (SDN) simplifies network management tasks by separating the control plane. However, the increased network traffic results in energy and Quality of Service (QoS) efficiency issues, whereas IoT devices are susceptible to failures and attacks that have serious security consequences. In this regard, providing a guarantee that SDN routing satisfies energy, QoS and security related policies is crucial for the network management. In this paper, we propose a policy-based framework aiming to verify that SDN routing decisions are optimal regarding energy, QoS and security properties. The proposed framework will enable the IoT network operator to adjust the policy constraints according to the demands of each use case (e.g., aiming at more secure or faster network). Finally, our framework is illustrated using a representative evaluation scenario.
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