A THEORETICAL FRAMEWORK INTEGRATING TRANSFORMERS, DIFFUSION MODELS AND INTRUSION DETECTION SYSTEMS TO ENHANCE INTERNET OF THINGS SECURITY

Abstract

The Internet of Things (IoT) is rapidly transforming numerous industries by integrating a vast number of devices, enhancing automation, and providing real-time, meaningful data. However, as connectivity grows, IoT systems are vulnerable to a variety of cybersecurity threats. Because of these limitations, traditional intrusion detection systems that rely on static, signature-based techniques are frequently ill equipped to handle the dynamic nature of modern cyber threats in Internet of Things environments. As a result, new tactics needed to defend IoT networks against sophisticated threats like Distributed Denial of Service (DDoS) attacks and zero-day attacks. This study examines advanced intrusion detection systems (IDS) strategies that employ cutting-edge technology and their integration to address these issues. In particular, transformers are quite good at identifying intricate and multi-phase attack methods by evaluating sequential data from Internet of Things networks. The issue of imbalanced or insufficient datasets resolved by Diffusion Models, which improve IDS training by producing synthetic attack data. Additionally, Explainable Artificial Intelligence (XAI) ensures transparency and confidence in IDS picks, while edge computing enables real-time threat detection by internally processing data and improving scalability. With the help of these advancements, IDS can become more adaptable and resilient, offering complete security solutions tailored to the unique needs of IoT networks.