Design of an Ultra-Light Weight Cryptographic algorithm for heterogeneous environment in the Internet of Things

Abstract

The Internet of Things (IoT) has revolutionized our lives by creating a smart infras tructure using various devices capable of self-organization. However, this interconnected network raises concerns about data privacy and protection. Moreover, the limited re sources and battery power of IoT devices necessitate developing resource-optimized and secure solutions. To address these issues, this research proposes an integrated communi cation protocol using symmetric key-based cryptography and a Deep Learning Convolu tional Neural Network (DLCNN) for predicting normal and attacked data. The logical map generates the symmetric keys, ensuring resistance against key reset and device cap ture attacks, resulting in an Ultra-Lightweight Communication (ULWC) protocol with improved attack detection parameters. In addition to the communication protocol, this work focuses on enhancing IoT secu rity through Lightweight Cryptography-based Attribute-Based Encryption (LWC-ABE) method. The proposed LWC-ABE method reduces the reliance on multiple trusted au thority environments, which can be bottlenecks in IoT servers and devices. It offers high expressiveness, access policy updates, large attribute domains, and white box trace ability properties. Simulation results demonstrate that the proposed LWC-ABE method outperforms conventional approaches, with reduced encryption and decryption times for multi-users and different message sizes. Finally, Lightweight-Medical Image Cryptography (LW-MIC) system was developed using ELWC protocols. The medical image data is first converted into digital format, and then ELWC operations, employing Play-Fair and Cha-Cha based encryption algorithms, are applied to the vector data. This ensures that the secured image data transmitted over the Internet of Medical Things (IoMT) environment remains protected. At the receiver’s end (doctor), the ELWC decryption algorithms restore the original image data. Abstract vii The simulation results for the proposed ULWC, LWC-ABE, and LW-MIC protocols indicate superior security performance compared to state-of-the-art methods. Addition ally, these solutions demonstrate reduced time complexity, making them efficient and effective for securing IoT environments