School of Information Technology and Engineering

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http://etd.aau.edu.et/handle/123456789/460

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Browsing School of Information Technology and Engineering by Author "Henock Mulugeta (PhD)"

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    Lightweight Intrusion Detection System for IoT with Improved Feature Engineering and Advanced Dynamic Quantization
    (Addis Ababa University, 2024-11) Semachew Fasika; Henock Mulugeta (PhD)
    In recent years, the proliferation of Internet of Things (IoT) devices and applications has experienced a significant surge globally, owing to their inherent advantages in enhancing both business and industrial landscapes, as well as facilitating improvements in individuals’ daily routines. Nevertheless, IoT devices are not immune to malicious attacks, which results potential negative consequences and malfunctioning of IoT devices, therefore, attack detection and classification becomes an important issue in IoT devices. This research proposes a lightweight hybrid deep learning model (DNN-BiLSTM) to detect and classify attacks in an IoT system with improved feature engineering and advanced quantization. Although leveraging hybrid deep learning model which combines DNN alongside BiLSTM, facilitates the extraction of intricate network features in a nonlinear and bidirectional manner, aiding in the identification of complex attack patterns and behaviors, its implementation on IoT devices remains challenging. To mitigate the constraints inherent in IoT devices, this research incorporates improved feature engineering, specifically Redundancy-Adjusted Logistic Mutual Information Feature Selection (RAL-MIFS) combined with a two-stage IPCA algorithm. Additionally, advanced quantization (QAT + PTDQ) techniques, alongside advanced Optuna for hyperparameter optimization, are utilized to enhance computational efficiency without compromising detection accuracy. Experimental evaluations were conducted on the CIC IDS2017 and CICIoT2023 datasets to assess the performance of a quantized DNN-BiLSTMQ model. The model demonstrated superior detection accuracy & computational efficiency compared to state-ofthe- art methods. On the CIC IDS2017 dataset, it achieved a detection accuracy of 99.73% with a model size of 25.6 KB, while on the CICIoT2023 dataset, it achieved a detection accuracy of 93.95% with a model size of 31.3 KB. These results highlight the potential of the quantized DNN-BiLSTMQ model for efficient and accurate cyber attack detection on IoT.
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    Lightweight IOT Security With Deep Learning-Driven Biometric for Human Authentication
    (Addis Ababa University, 2025-02) Girma Alemu; Henock Mulugeta (PhD)
    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.