Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models

• URL: http://arxiv.org/abs/2501.13962v1
• Date: Tue, 21 Jan 2025 20:52:23 GMT
• Title: Adaptive Cyber-Attack Detection in IIoT Using Attention-Based LSTM-CNN Models
• Authors: Afrah Gueriani, Hamza Kheddar, Ahmed Cherif Mazari,
• Abstract summary: This study presents the development and evaluation of an advanced Intrusion detection (IDS) based on a hybrid LSTM-convolution neural network (CNN)-Attention architecture. The research focuses on two key classification tasks: binary and multi-class classification. In binary classification, the model achieved near-perfect accuracy, while in multi-class classification, it maintained a high accuracy level (99.04%), effectively categorizing different attack types with a loss value of 0.0220%.
• Score: 0.23408308015481666
• License:
• Abstract: The rapid expansion of the industrial Internet of things (IIoT) has introduced new challenges in securing critical infrastructures against sophisticated cyberthreats. This study presents the development and evaluation of an advanced Intrusion detection (IDS) based on a hybrid LSTM-convolution neural network (CNN)-Attention architecture, specifically designed to detect and classify cyberattacks in IIoT environments. The research focuses on two key classification tasks: binary and multi-class classification. The proposed models was rigorously tested using the Edge-IIoTset dataset. To mitigate the class imbalance in the dataset, the synthetic minority over-sampling technique (SMOTE) was employed to generate synthetic samples for the underrepresented classes. This ensured that the model could learn effectively from all classes, thereby improving the overall classification performance. Through systematic experimentation, various deep learning (DL) models were compared, ultimately demonstrating that the LSTM-CNN-Attention model consistently outperformed others across key performance metrics. In binary classification, the model achieved near-perfect accuracy, while in multi-class classification, it maintained a high accuracy level (99.04%), effectively categorizing different attack types with a loss value of 0.0220%.

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