## 1 Introduction  
The proliferation of Internet of Things (IoT) devices has increased significantly recently. The estimated number of active IoT devices in 2019 was approximately 26.6 billion, with a projected increase to 30 billion in 2020, according to SafeAttLast. According to the International Data Corporation (IDC), the number of interconnected IoT devices will exceed 41.6 billion by 2025, resulting in an estimated 79.4 ertabytes (ZB) of data. The need for secure, efficient, and scalable communication protocols has been underscored by the ongoing expansion of IoT ecosystems in a variety of sectors, including healthcare, industrial automation, and smart cities. Due to its lightweight architecture, low power consumption, and robust scalability, Zigbee is a standout among widely adopted IoT communication protocols. Healthcare monitoring systems, industrial control systems, and smart home automation have widely implemented Zigbee, which is based on the IEEE 802.15.4 standard. It offers a solution for IoT networks that is both energy efficient and cost effective, particularly in environments that require short-range, low-data-rate communication [1]. Despite its numerous benefits, Zigbee suffers from a number of critical security vulnerabilities, including key distribution, authentication, and network integrity. Due to these problems, apps enabled by IoT are at high risk of being hacked or spied on through key compromise, DoS attacks, Man in the Middle Attacks, and other threats. Key management is a fundamental security concern in Zigbee networks. For network authentication, the protocol depends on a globally shared master secret. If this key is compromised, it can result in unauthorized access and network-wide breaches. Additionally, Zigbee's cryptographic mechanisms and device limitations make it open to both passive and active attacks, which means that the current security system can be broken into. Attacks like the Philips Hue malware exploit and others in the real world have shown that attackers can get into and control whole smart lighting networks by using broken key distribution mechanisms. Additionally, these security risks are further exacerbated by the absence of secure authentication methods and robust end-to-end encryption. In order to overcome these obstacles, this paper suggests the integration of Radial Basis Function (RBF) networks and blockchain technology to create an Intelligent Hybrid Framework for Threat Pre-Identification and Secure Key Distribution in Zigbee-Enabled IoT Networks [2]. The RBF network, a specialized form of artificial neural network, is employed for anomaly detection, allowing for the real-time identification of potential security hazards prior to their ability to corrupt the network. Simultaneously, blockchain technology guarantees decentralized and tamper-proof key management, thereby eradicating single points of failure in key distribution processes. When these two technologies are combined, they make Zigbee-enabled IoT networks stronger against cyber threats. This also makes sure that security operations are scalable and effective. The proposed framework introduces a Blockchain Security System (BCS) that is trust-based and enables the secure distribution of cryptographic credentials across Zigbee-enabled IoT devices. The framework improves the security of key storage by utilizing physically unclonable functions (PUFs), rendering it resistant to unauthorized duplication and replication. Furthermore, it can support secure network joining, key distribution across the whole network, key updates, and access control mechanisms, ensuring a complete security plan for IoT applications. Our approach makes Zigbee networks more secure by combining blockchain-based key management with RBF-based threat intelligence. This lowers the risks that come with using regular key distribution methods. This mixed security model makes sure that large-scale deployments work well and can talk to each other. It also keeps IoT devices safe from new cyber threats.