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<p>In the era of interconnected technologies, the ongoing threat of intrusion attacks poses significant risks, exposing individuals and organizations to potentially severe repercussions. Network Intrusion Detection System (IDS) has a crucial role in securing these networks. In this paper, we propose an hybrid model combining the convolution neural network (CNN) and the long short-term memory (LSTM) with hyper-parameters optimization using Bayesian optimization method for attacks detection in IoT. We train and test our hybrid model using the benchmark dataset UNSW-NB15 for evaluating its effectiveness in intrusion detection. The performance of our model is assessed using various metrics, including accuracy, precision, recall, and F1-score. In addition, we conduct a comparative study to highlight the effectiveness of our proposed model, which achieved remarkable accuracy on the testing set. This evaluation underscores the model's potential for attacks detection.</p>

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Intelligent network intrusion detection system using optimized deep CNN-LSTM with UNSW-NB15

  • 期刊论文

  • 发表日期：

    2025年2月4日

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  • Adel Thaljaoui

摘要

In the era of interconnected technologies, the ongoing threat of intrusion attacks poses significant risks, exposing individuals and organizations to potentially severe repercussions. Network Intrusion Detection System (IDS) has a crucial role in securing these networks. In this paper, we propose an hybrid model combining the convolution neural network (CNN) and the long short-term memory (LSTM) with hyper-parameters optimization using Bayesian optimization method for attacks detection in IoT. We train and test our hybrid model using the benchmark dataset UNSW-NB15 for evaluating its effectiveness in intrusion detection. The performance of our model is assessed using various metrics, including accuracy, precision, recall, and F1-score. In addition, we conduct a comparative study to highlight the effectiveness of our proposed model, which achieved remarkable accuracy on the testing set. This evaluation underscores the model's potential for attacks detection.