Abstract

Internet security is of paramount importance due to the pervasive nature of the network in modern society. As the globe grows increasingly interconnected, issues like data breaches, unauthorized access, and service disruptions become more common. Safeguarding private data, ensuring uninterrupted communication, and protecting vital services are all essential to establishing confidence and stability in the online world. Internet security is a complex problem to solve due to the interconnected nature of the Internet's Architecture and Protocols (IAP). Due to the wide variety of devices and platforms that can access the Internet, cybercriminals can breach a complex ecosystem. Constant monitoring and flexibility are required due to the rapid development of new attack methods and vulnerabilities. The difficulty lies in balancing implementing new security measures and minimizing disruptions to the user experience, which calls for adaptive and novel approaches. In this paper, the Behavioural Biometric Block Chain-Enhanced Authentication layer (BBB-EAL) framework recommends a static authentication mechanism for end-users and edge servers. This authentication creates a secure and encrypted link between parties. Access tokens are produced via a smart contract, removing the requirement for a trusted third party. This work emphasizes the importance of architecture design and sequence diagrams in representing participant interactions and information sharing. Additionally, it examines the construction of the Machine Learning (ML) model used to recognize KMT dynamics. Simulations indicate that the recommended design improves user authentication in an IAP-enabled environment. The findings demonstrate the ability to evaluate confidence in real time, achieve minimal authentication time, and utilize resources efficiently.