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Automatic Network Intrusion Detection System Using Machine learning and Deep learning
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  • Mohammed Mynuddin ,
  • Sultan Uddin Khan ,
  • Zayed Uddin Chowdhury ,
  • Foredul Islam,
  • Md Jahidul Islam ,
  • Mohammad Iqbal Hossain ,
  • Dewan Mohammed Abdul Ahad
Mohammed Mynuddin
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Sultan Uddin Khan

Corresponding Author:[email protected]

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Zayed Uddin Chowdhury
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Foredul Islam
Md Jahidul Islam
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Mohammad Iqbal Hossain
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Dewan Mohammed Abdul Ahad


In recent years, the popularity of network intrusion detection systems (NIDS) has surged, driven by the widespread adoption of cloud technologies. Given the escalating network traffic and the continuous evolution of cyber threats, the need for a highly efficient NIDS has become paramount for ensuring robust network security. Typically, intrusion detection systems utilize either a pattern-matching system or leverage machine learning for anomaly detection. While pattern-matching approaches tend to suffer from a high false positive rate (FPR), machine learning-based systems, such as SVM and KNN, predict potential attacks by recognizing distinct features. However, these models often operate on a limited set of features, resulting in lower accuracy and higher FPR. In our research, we introduced a deep learning model that harnesses the strengths of a Convolutional Neural Network (CNN) combined with a Bidirectional LSTM (Bi-LSTM) to learn spatial and temporal data features. The model, evaluated using the NSL-KDD dataset, exhibited a high detection rate with a minimal false positive rate. To enhance accuracy, K-fold cross-validation was employed in training the model. This paper showcases the effectiveness of the CNN with Bi-LSTM algorithm in achieving superior performance across metrics like accuracy, F1-score, precision, and recall. The binary classification model trained on the NSLKDD dataset demonstrates outstanding performance, achieving a high accuracy of 99.5% after 10-fold cross-validation, with an average accuracy of 99.3%. The model exhibits remarkable detection rates (0.994) and a low false positive rate (0.13). In the multiclass setting, the model maintains exceptional precision (99.25%), reaching a peak accuracy of 99.59% for k-value=10. Notably, the Detection Rate for k-value=10 is 99.43%, and the mean False Positive Rate is calculated as 0.214925.
04 Mar 2024Submitted to TechRxiv
06 Mar 2024Published in TechRxiv