Lightweight IOT Security With Deep Learning-Driven Biometric for Human Authentication
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Date
2025-02
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Addis Ababa University
Abstract
Now today the number of Internet of Things (IoT) devices increases in number, as the number of IoT device increase there is also a rise in risk with these IoT devices. IoT devices have a great impact on daily lives of human being. Huge number of data can be stored, transmitted and used through IoT devices. Some of the data are very sensitive which are vulnerable to different attacks. To protect IoT devices from these attacks, different counter measures are conduct through previous researches. Conventional biometric authentication methods like possession-based (tokens) and knowledge-based (passwords/PINs) are used to tackle the problem of access control which are prone to loss, duplication, guesswork, and forgetfulness. Similarly, single-modality biometric identification—like fingerprint or facial recognition—is insufficient due to its susceptibility to spoofing attacks. When merging and comparing large amounts of biometric data, it is important to consider variations in the quantity and caliber of data sources, even though multi-biometric systems improve security. Our proposed solution to these problems combines a lightweight deep learning algorithm designed for Internet of Things devices with multimodal biometrics that are using fingerprint and face. By conducting an experiment on both training and unseen datasets, the model demonstrated good classification ability with 82.5% validation accuracy and 99.3% training accuracy. The suggested solution addresses the security issues of IoT devices through modeling and experimental validation. Through hands-on testing, we assessed the system's performance, and the outcomes showed a robust IoT security solution. In the end, the combination of deep learning algorithms and dual biometric modalities has greatly improved secure authentication procedures for IoT applications. At the end, secure authentication techniques for IoT applications have advanced significantly with the combination of deep learning algorithms and dual biometric modalities.
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Keywords
Biometrics, Deep Learning, IoT, Light weight CNN, MobileNetV2