Intrinsically explainable security intelligence
2024
In today's interconnected world, the proliferation of Internet of Things (IoT) devices has ushered in unprecedented convenience and connectivity. Yet, amid this technological marvel lies a pressing concern - the security of these interconnected systems. As IoT systems become increasingly intertwined with our daily lives, ensuring their security has emerged as a critical priority. At the heart of this challenge lies the need for transparency and interpretability in the artificial intelligence (AI) systems governing these IoT networks [1]. Intrinsically explainable AI (XAI) stands as a beacon of hope in addressing the complexities of ensuring IoT security. Unlike traditional black-box AI algorithms, XAI approaches offer a fundamental shift by emphasizing interpretability and transparency as integral components of AI models. Specifically tailored for IoT systems, these intrinsically explainable methods hold the promise of not only delivering robust security measures but also empowering users and stakeholders with insights into the decision-making processes of these interconnected devices. However, the journey toward fortified IoT security via AI is marred by a series of challenges stemming from the opacity and lack of interpretability in existing AI systems. As IoT devices multiply, the reliance on AI for managing, analyzing, and securing the vast data streams they generate grows exponentially. Yet, this reliance often comes at the cost of understanding how and why AI-driven decisions are made within these systems. The black-box nature of many AI algorithms poses a substantial barrier, hindering the comprehension of crucial security-related actions, leaving users and administrators in the dark regarding potential vulnerabilities and threat patterns [2]. Moreover, within the context of IoT security, the lack of transparency amplifies concerns about accountability and trustworthiness. As these AI-driven systems autonomously make decisions that impact security protocols, the inability to decipher their decision-making rationale raises significant apprehensions. It complicates identifying and mitigating vulnerabilities and raises ethical and regulatory issues surrounding liability and responsibility in the event of security breaches or system malfunctions. In addressing these challenges, the quest for intrinsically XAI methods becomes imperative. By bridging the gap between complex AI algorithms and human comprehension, these methods offer a pathway to fortify IoT security while simultaneously ensuring a deeper understanding of the decision-making processes driving these systems. Through this lens, this chapter embarks on an exploratory journey into the realm of intrinsically XAI methods within the context of IoT security. In this chapter, the focus revolves around categorizing intrinsically XAI models into four primary categories, each representing a distinct approach toward achieving interpretability and transparency in AI systems. These categories encompass linear models, graphical models, tree-based models, and K-nearest neighbors (KNNs), each offering unique characteristics and advantages in facilitating explainability [3].