Department of Computational Intelligence, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, 632014, Tamil Nadu, India.
The Internet of Things (IoT) represents a vast network of interconnected devices, from simple sensors to intricate machines, which collect and share data across sectors like healthcare, agriculture, and home automation. This interconnectivity has brought convenience and efficiency but also introduced significant security concerns. Many IoT devices, built for specific functions, may lack robust security, making them vulnerable to cyberattacks, especially during device-to-device communications. Traditional security approaches often fall short in the vast and varied IoT landscape, underscoring the need for advanced Anomaly Detection (AD), which identifies unusual data patterns to warn against potential threats. Recently, a range of methods, from statistical to Deep Learning (DL), have been employed for AD. However, they face challenges in the unique IoT environment due to the massive volume of data, its evolving nature, and the limitations of some IoT devices. Addressing these challenges, the proposed research recommends using autoencoders with a dynamic threshold mechanism. This adaptive method continuously recalibrates, ensuring relevant and precise AD. Through extensive testing and comparisons, the study seeks to demonstrate the efficiency and adaptability of this approach in ensuring secure IoT communications.
Keywords
Internet of Things, Anomaly Detection, Cyber Attacks, Autoencoders, Security, Accuracy.
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Sudhakar Sengan
Sudhakar Sengan
Department of Computer Science and Engineering, PSN College of Engineering and Technology, Tirunelveli, 627451, Tamil Nadu, India.
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Cite this article
Nayer Tumi Figueroa E, Vishnu Priya A, Selvanayaki Kolandapalayam Shanmugam, Kiran Kumar V, Sudhakar Sengan and Alexandra Melgarejo Bolivar C, “Adaptive Approach to Anomaly Detection in Internet of Things Using Autoencoders and Dynamic Thresholds”, Journal of Machine and Computing, pp. 001-010, January 2024. doi: 10.53759/7669/jmc202404001.
