## 1 Introduction  
Our society is increasingly witnessing the integration of diverse smart environments driven by Internet of Things (IoT) solutions. The concepts of smart cities, streamlined processes in smart environments, and efficient patient monitoring and treatment in smart healthcare are frequently discussed. According to the Ericsson Mobility Report [1], IoT connections are projected to rise to 38.9 billion by 2029, a significant increase from the current 15.7 billion. The number of IoT connections will more than double in the next five years. These expansive networks manage substantial volumes of sensitive data, rendering them attractive targets for cyber threats [2], thus necessitating tightening security measures in such networks. The proliferation of IoT organizations and alliances attempting to enforce diverse protocols and security measures contributes to the complexity and credibility challenges in IoT security [3]. Researchers are actively developing innovative solutions to bolster IoT security despite these obstacles. As highlighted in [4], the primary research domains include blockchain, fog computing, machine learning (ML), edge computing, and encryption-based solutions. Blockchain-based security operates as a distributed ledger with chronological, time-stamped entries. Each entry is linked to the previous one using a cryptographic hash function, and individual transactions are stored in a Merkle tree [4, 5]. However, the decentralized nature required by blockchain poses significant challenges in IoT networks due to resource limitations in the majority of nodes. Implementing extensive blockchain-based security, including address management, communications, and distribution, becomes complex in such environments [6]. Cloud computing can address a limited scope of IoT issues, leading to Cisco's introduction of fog computing in 2012 to supplement its capabilities [4]. While cloud storage boasts accessibility advantages, it faces the considerable drawback of vulnerability to data breaches, especially due to its centralized nature. Fog computing represents a middle-ground solution, leveraging nodes closer to end devices. This setup facilitates data processing at these intermediary nodes, reducing the amount of traffic between devices and the cloud. Although this setup does offer advantages by addressing some latency issues, it does not entirely resolve vulnerabilities between the end devices and the fog layer [7]. Edge computing introduces a significant shift by moving most processing to end devices. This approach significantly improves latency by localizing data processing but necessitates substantially higher resource allocation and computational power. However, this presents a security concern, as all data resides on these end-user devices, making the network vulnerable to misconfigurations and potential security breaches [8]. ML offers the capability to detect intrusions and anomalies in IoT networks. However, its widespread use faces limitations, including the selection and training of appropriate ML algorithms and the complex task of preparing IoT-generated data for accurate predictions. The abundance of data from IoT devices, often riddled with outliers and missing values, poses challenges for ML efficiency, demanding meticulous cleaning and preprocessing [4; 9; 10]. Encryption plays a vital role in the IoT, though it does not prevent security risks. Implementing link-level symmetric-key encryption like DES or AES ensures secure transmission; however, in this case, the system requires key distribution mechanisms. That can be implemented by applying asymmetric-key approaches such as RSA or elliptic-curve-based cryptography, yet managing keys across vast networks is complex, especially in decentralized settings. IoT devices, often resource-constrained and real-time-reliant, need help with traditional encryption's demands [11]. Lightweight cryptography offers a simpler alternative but requires new algorithms to balance simplicity with robustness.