Correlation Composition Awareness Model with Pair Collaborative Localization for IoT Authentication and Localization

Abstract

Secure authentication and accurate localization among Internet of Things (IoT) sensors are pivotal for the functionality and integrity of IoT networks. IoT authentication and localization are intricate and symbiotic, impacting both the security and operational functionality of IoT systems. Hence, accurate localization and lightweight authentication on resource-constrained IoT devices pose several challenges. To overcome these challenges, recent approaches have used encryption techniques with well-known key infrastructures. However, these methods are inefficient due to the increasing number of data breaches in their localization approaches. This proposed research efficiently integrates authentication and localization processes in such a way that they complement each other without compromising on security or accuracy. The proposed framework aims to detect active attacks within IoT networks, precisely localize malicious IoT devices participating in these attacks, and establish dynamic implicit authentication mechanisms. This integrated framework proposes a Correlation Composition Awareness (CCA) model, which explores innovative approaches to device correlations, enhancing the accuracy of attack detection and localization. Additionally, this framework introduces the Pair Collaborative Localization (PCL) technique, facilitating precise identification of the exact locations of malicious IoT devices. To address device authentication, a Behavior and Performance Measurement (BPM) scheme is developed, ensuring that only trusted devices gain access to the network. This work has been evaluated across various environments and compared against existing models. The results prove that the proposed methodology attains 96% attack detection accuracy, 84% localization accuracy, and 98% device authentication accuracy.