IoT threat mitigation engine empowered by artificial intelligence multi-objective optimization

Abstract

From smart homes to smart cities and Industry 4.0 to Transportation Systems, Internet of Things (IoT) is a domain which promises incredible growth coupled with great impact, in numerous fields. IoT networks are composed of numerous different Things, arranged in diverse topologies with diverse needs. This diversity is partially due to the numerous areas where IoT applications are utilized, which at their entirety can be referred as the IoT ecosystem. The IoT ecosystem suffers from numerous vulnerabilities, due to reasons such as design flows, hardware limitation or simply human error and is subject to various attacks targeting IoT services, platforms and networks. These attacks can have significant consequences such as economic losses, service disruption or data leaks. Cyber-attacks are an unavoidable and must be faced in tandem with the global growth of IoT networks. An approach that can assist in developing robust, intelligent Cyber-security tools for IoT is using Artificial Intelligence. In the following paper, a mechanism is presented that automatically selects appropriate mitigation actions in an optimal way to countermeasure attacks faced by IoT networks. This is achieved by using an novel Artificial Intelligence mechanism based on a Deep Neural Architecture called Pointer Networks to optimize security-related KPIs. Experimental results, show that the proposed method produces equal or better Pareto optimal solutions, performs faster compared to state-of-the-art (SoA) algorithms and scales better.