School of Information Technology and Engineering
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Browsing School of Information Technology and Engineering by Author "Semachew Fasika"
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Item 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.