Enhancing IoT Data Security through Multilayered Approach: Chaotic Map, DNA Sequencing, Blockchain Intergration

Abstract

The rapid proliferation of the Internet of Things (IoT) devices has ushered in an era of unprecedented connectivity, yet it also poses significant challenges in terms of data security and privacy. In this research, a robust and innovative approach is proposed to address these challenges by introducing a multi-layered security model integrating three powerful technologies: chaotic maps, DNA computing, and block chain technology to fortify IoT data protection. The key objective of proposed technique is to offer security to data at multiple levels while minimizing the Encryption Time (ET) and Decryption Time (DT). In the proposed work, two types of inputs i.e., textual data and medical image data is taken upon which proposed technique is implemented. The key innovation lies in the generation of binary keys from chaotic maps, making it different from traditional chaotic key methods, thereby establishing a unique and robust foundation for encryption. Furthermore, to enhance the encryption process, a dual layer of security is implemented through DNA sequencing and blockchain, ensuring an unprecedented level of data integrity and confidentiality. Notably, the blockchain phase utilizes SHA-256 for generating hash values, fortifying the blocks with an additional layer of cryptographic strength. This novel approach offers a comprehensive and sophisticated solution, effectively mitigating security concerns in IoT ecosystems. By combining the power of chaotic maps, DNA encoding, and blockchain technology, proposed model stands at the forefront of IoT data security, providing a resilient defence against contemporary and future threats.The performance of proposed approach is examined and validated in terms of ET and DT in MATLAB software. Simulating results revealed that proposed model is outperforming other similar models by attaining lowest ET and DT time for both text and image data.