Enhancing embedded systems security: Integrating machine learning for cyber threat mitigation

Almost every modern electronic device relies on some form of embedded system, but as these systems have become more sophisticated, they are increasingly prone to cyber attack. This paper focuses on an artificial intelligence technique to improve cybersecurity in embedded systems. First, we define common microcontroller and embedded app vulnerabilities. This paper introduces a novel methodology applying machine learning algorithms to automatically detect anomalies and predict intrusions, which not only improves security but also increases the resilience of these systems against cyber-attacks. 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.