Enhanced IoT Security: Blockchain, Cryptography, and CNN Integration for Mask Detection

Abstract

Security concerns have become paramount, especially with the increasing reliance on facial recognition technology for identity verification. The use of masks, while essential for health protection, poses a significant challenge for these systems, hindering accurate identification. This issue is particularly critical for organizations that depend on facial recognition for security and authentication, especially within IoT systems that are vulnerable to malicious activities. This article presents two major contributions to address these challenges. First, it enhances the detection of mask-wearing individuals using a computer vision machine-learning model, specifically a convolutional neural network (CNN). Second, it introduces a novel authentication framework for verifying users during communication between nodes and access points. The authentication system comprises three key phases. Initially, an advanced fusion technique at the access point level employs a unique hybrid biometric pattern that combines password and image features to bolster security. Next, secure transmission of this pattern between the node and the access point is ensured through AES cryptography and blockchain technology. Finally, a novel matching method compares password and image features against database records for verification, conducted during the development phase at the node level. Experimental analysis reveals that this approach delivers outstanding results, achieving 99% accuracy, 0.99 recall, 100% precision, and an F1 score of 0.989.