Enhancing IoT Security with Lightweight Cryptographic Operations Using Temporal Spatial Hyperdimensional Computing

Abstract

Introduction: Network-based security challenges related to the Internet of Things (IoT) are rising, network-based security challenges have become more prominent, raising concerns about the vulnerability of systems to severe security threats. Cyberattacks such as command injection, denial of service, surveillance, and backdoors exploit abnormal patterns in network behavior. Traditional machine learning techniques, including logistic regression and feature-based support vector machines, have been integrated with end-to-end deep neural networks to enhance intrusion detection. However, these approaches struggle with small sample sizes and fail to adapt efficiently to evolving threats and dynamic IoT environments. Additionally, the resource constraints of IoT devices necessitate secure and efficient cryptographic solutions to ensure data integrity and confidentiality.

Objectives: The primary objective of this study is to develop a robust and adaptive cryptographic framework that addresses the challenges of lightweight security in IoT environments. The proposed Temporal-Spatial Hyper Dimensional Computing (TS-HDC) method aims to enhance key generation, encryption, and authentication by incorporating time-dependent and location-specific data. This novel approach seeks to mitigate risks such as key reuse, replay attacks, and unauthorized access while maintaining low computational and energy costs suitable for resource-constrained IoT devices.

Methods: The TS-HDC framework leverages high-dimensional vectors combined with dynamic geographical and temporal data encoding to enhance cryptographic adaptability. Hyper vectors with embedded contextual information allow real-time adjustments in security processes based on the IoT environment. The system's efficiency was evaluated using the WUSTL-IIOT-2021 dataset, where various cryptographic metrics, including key strength, computational overhead, and resistance to attacks, were analyzed. Performance comparisons with traditional cryptographic techniques were conducted to assess improvements in scalability, efficiency, and security.

Results: The experimental evaluation demonstrated that TS-HDC significantly enhances the security of IoT networks by dynamically adjusting cryptographic functions in response to environmental changes. The method outperformed conventional cryptographic solutions in terms of adaptability, energy efficiency, and protection against attacks such as key reuse and replay exploits. Results from WUSTL-IIOT-2021 trials indicated a notable reduction in computational overhead, making TS-HDC a viable security solution for IoT applications with limited processing power and battery life.

Conclusion: The proposed TS-HDC framework provides a scalable and efficient cryptographic solution for securing IoT devices against emerging threats. By integrating temporal and spatial factors into cryptographic processes, it ensures enhanced adaptability to dynamic IoT environments while maintaining low computational costs. The findings highlight the potential of TS-HDC in securing IoT applications across various domains, including smart homes, healthcare, and industrial systems. Future research will explore further optimizations and real-world deployments to strengthen IoT security against evolving cyber threats.