Lightweight CNN-BiLSTM based Intrusion Detection Systems for Resource-Constrained IoT Devices

• URL: http://arxiv.org/abs/2406.02768v1
• Date: Tue, 4 Jun 2024 20:36:21 GMT
• Title: Lightweight CNN-BiLSTM based Intrusion Detection Systems for Resource-Constrained IoT Devices
• Authors: Mohammed Jouhari, Mohsen Guizani,
• Abstract summary: Intrusion Detection Systems (IDSs) have played a significant role in detecting and preventing cyber-attacks within traditional computing systems. The limited computational resources available on Internet of Things (IoT) devices make it challenging to deploy conventional computing-based IDSs. We propose a hybrid CNN architecture composed of a lightweight CNN and bidirectional LSTM (BiLSTM) to enhance the performance of IDS on the UNSW-NB15 dataset.
• Score: 38.16309790239142
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Intrusion Detection Systems (IDSs) have played a significant role in detecting and preventing cyber-attacks within traditional computing systems. It is not surprising that the same technology is being applied to secure Internet of Things (IoT) networks from cyber threats. The limited computational resources available on IoT devices make it challenging to deploy conventional computing-based IDSs. The IDSs designed for IoT environments must also demonstrate high classification performance, utilize low-complexity models, and be of a small size. Despite significant progress in IoT-based intrusion detection, developing models that both achieve high classification performance and maintain reduced complexity remains challenging. In this study, we propose a hybrid CNN architecture composed of a lightweight CNN and bidirectional LSTM (BiLSTM) to enhance the performance of IDS on the UNSW-NB15 dataset. The proposed model is specifically designed to run onboard resource-constrained IoT devices and meet their computation capability requirements. Despite the complexity of designing a model that fits the requirements of IoT devices and achieves higher accuracy, our proposed model outperforms the existing research efforts in the literature by achieving an accuracy of 97.28\% for binary classification and 96.91\% for multiclassification.

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