EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR CYBERATTACK DETECTION IN INTERNET OF THINGS NETWORKS: A DEEP LEARNING-BASED FRAMEWORK FOR SECURE AND TRUSTWORTHY SYSTEMS

Abstract

The Internet of Things, or IoT, has revolutionized today's digital ecosystem, creating a seamless IoT web of interconnections between critical infrastructures, healthcare, manufacturing, and transportation, as well as smart cities. With the growing interconnectedness of today, however, have come new and complex cybersecurity challenges that are difficult to detect using traditional methods. Deep Learning models have shown high accuracy in Cyberattack Detection, but their black-box nature hinders Transparency and Interpretability and reduces user confidence, which is one of the reasons why they are not used in security-critical environments. This paper explores the potential and impact of Explainable Artificial Intelligence (XAI) in deep learning cyberattack detection systems for IoT networks to improve their effectiveness and trustworthiness. In this research, a Qualitative Research methodology is used, which is systematically exploring the latest scholarly research and conducting thematic analysis of different studies related to Artificial Intelligence, Cyber Security and IoT security. The evidence gathered is then aggregated to uncover common research themes, challenges in implementing XAI, methods of making it explainable, and new research avenues for incorporating XAI within intelligent intrusion detection systems. The results show that XAI can effectively explain the results of AI-based detection systems, making them more interpretable and understandable, and helping cybersecurity practitioners interpret, validate, and justify the decisions made by the AI system. The analysis also emphasizes the benefits of explainable models, such as increasing trust among stakeholders, ensuring regulatory compliance, strengthening incidents responses, and alleviating uncertainties for automated security decisions. These benefits are not without their drawbacks, however, with issues of computational burden, scalability, model complexity, and the lack of standardized frameworks to measure explainability. The study provides a detailed conceptual framework which explains the way XAI can enhance Trustworthy AI and increase transparency, interpretability, and accountability of decisions in IoT-based CyberAttack Detection Systems. The results provide theoretical guidance for future research into explainable cybersecurity and give direction for creating more robust and secure AI-based defense methods. Future studies are needed to test explainable detection frameworks in real IoT applications, to create common metrics to evaluate them, and to create scalable XAI approaches that can support future Secure Systems.