Lightweight Intrusion Detection System for IoT with Improved Feature Engineering and Advanced Dynamic Quantization
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Date
2024-11
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Addis Ababa University
Abstract
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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Keywords
Attack detection, Cybersecurity, Cyber attack, Intrusion, Lightweight deep learning, Feature engineering, IoT, DNN, BiLSTM, Advanced Dynamic Quantization and Optimization, RAL-MIFS