Archives of Advances in Artificial Intelligence & Data Science and Machine Learning | Volume 1, Issue 1 | Review Article | Open Access
Soren Falkner*
Vienna University of Technology, Faculty of Computer Engineering, Vienna, Austria
*Correspondence to: Soren Falkner
Fulltext PDFThe increasing scale and complexity of Internet of Things (IoT) networks necessitate automated security response mechanisms to effectively mitigate threats in a timely manner. Manual intervention often proves insufficient to handle the volume and speed of potential attacks targeting vulnerable IoT devices and infrastructure. Artificial Intelligence (AI) offers a promising avenue for developing intelligent and autonomous security response systems capable of detecting, analyzing and reacting to security incidents in real-time. This paper explores the application of various AI techniques, including machine learning, rule-based expert systems and reinforcement learning, for automating security responses in IoT networks. We discuss the challenges and opportunities associated with implementing such systems, including the need for accurate threat detection, context-aware decision-making, secure actuation and minimizing disruption to normal operations. Furthermore, we examine potential AI-driven automated responses to common IoT threats, such as malware propagation, denial-of-service attacks and data breaches. Finally, we highlight future research directions focused on developing robust, adaptive and trustworthy AI-powered automated security response systems for the evolving IoT landscape.
Automated security response; Internet of things (IoT); Artificial intelligence (AI); Machine learning, Reinforcement learning; Expert systems; Intrusion response; Threat mitigation; Security automation; Cyber security; Network security; Edge computing; Real-time response
Falkner S. Automated Security Response in IoT Networks Using AI. Arch Adv Art Intel Data Sci Mach Learn 2025;1(1):1-12.