Securing embedded systems with machine learning: A modern approach to cyber threat defense

Almost all modern electronic devices rely on some form of embedded system, as illustrated in Figure 1., but as these systems become more sophisticated, they are more and more vulnerable to cyberattacks. This article focuses on an artificial intelligence technique aimed at improving cybersecurity in embedded systems. First, we define common vulnerabilities in micro-controllers and embedded applications. This article presents a new methodology that applies machine learning algorithms to automatically detect anomalies and predict intrusions, which not only improves security but also increases the resilience of these systems to cyberattacks. In a set of experiments, we show that our proposed approach is able to accurately spot malicious activities while minimizing false positives. We also present the implications of our findings for secure IoT system design and deployment in functions ranging from automotive to healthcare through industrial automation. Our study underlines that it is becoming increasingly important to introduce early cybersecurity responses and promote joint efforts in both research and industrial sectors against new cyber threats. Such insights are original and contain promising hints about how AI might strengthen embedded systems, motivating follow-on research exploring secure system design.Figure 1. Importance of embedded systems.Display full size