## 1 Introduction  
The Internet of Things (IoT) represents a paradigm shift in connectivity, extending beyond traditional computer-based Internet models to encompass a distributed, heterogeneous network of interconnected devices [1]. This revolutionary concept enables communication not only between people and machines but also among physical and virtual \"things\" themselves [2]. The IoT's potential to transform various sectors, from smart cities and agriculture to healthcare and manufacturing, is immense. However, this potential is accompanied by significant challenges, particularly in the realms of security, privacy, and device management. The proliferation of IoT devices in critical infrastructure, industrial systems, and healthcare environments [3, 4, 5, 6, 7, 8, 9, 10, 11] is giving rise to a new set of cybersecurity challenges. Recent attacks demonstrate how current security approaches are failing. For instance, the 2023 exploitation of vulnerable IoT sensors in water treatment facilities led to potential contamination risks [12], while compromised IoT devices in healthcare continue to expose sensitive patient data and disrupt critical care services [13]. Traditional security solutions struggle with three key limitations: they typically require substantial computational resourcesunsuited for resource-constrained IoT devices [14], they lack the ability to adapt in real time to emerging threats, and they operate in silos rather than providing comprehensive protection across the entire IoT ecosystem. Add to that the inherent constraints of many IoT devices, including limited computational capacity and power consumption requirements, which frequently limit and challenge the implementation of robust security measures [15]. Additionally, the absence of unified regulatory frameworks across jurisdictions has created inconsistent security standards, further exacerbating these vulnerabilities [16; 17; 18]  
These challenges are compounded by the emergence of new threat vectors. Quantum computing advances threaten to break current cryptographic protections [19], while AI-powered attacks are becoming increasingly sophisticated in exploiting IoT vulnerabilities [20]. Existing solutions either focus narrowly on specific security aspects like access control or threat detection, or they impose unsustainable computational and energy demands on IoT devices [7; 9; 11; 21; 22; 23; 24; 25]. There is a critical need for a unified security framework that can provide comprehensive protection while respecting the resource constraints of IoT devices, adapt dynamically to evolving threats, and ensure long-term resilience against emerging attack vectors. Henceforth, the integration of AI-driven techniques and edge-based architectures in IoT systems continues to evolve rapidly across various application domains. The complexity of the IoT ecosystem stems from its heterogeneous nature, combining various wired and wireless networks and connecting a diverse array of devices with differing capabilities and resources. This heterogeneity, while offering unprecedented opportunities for data collection and automation, also presents unique security and privacy challenges. The sheer scale of IoT deployments, with billions of devices generating vast amounts of data, further compounds these issues. Recent years have seen a surge in IoT-related cyber threats, driven by several factors. The increased complexity of IoT ecosystems, with multiple interconnected devices and systems, has made security management increasingly challenging. Supply chain attacks have become more prevalent [26], exploiting vulnerabilities in third-party components. Ransomware attacks targeting IoT devices have emerged as a significant threat [27], potentially disrupting critical systems. Additionally, the evolving regulatory landscape for IoT security [28] adds another layer of complexity for organisations striving to ensure compliance. Security in the IoT context extends beyond traditional cybersecurity concerns.