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An optimized Bi-LSTM with random synthetic over-sampling strategy for network intrusion detection

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Abstract

Due to the increased quantity and variety of cyber-attacks against networks, the conventional firewalls and encrypting data technologies are no longer sufficient to meet the network security requirements. As a consequence of this, the idea of using intrusion detection systems to combat network threats has been advocated. Machine learning aids common intrusion detection methods which show poor detection rates and requires wider feature engineering. This research offers the random synthetic over-sampling (RSO) Linguistic algorithm for detecting network intrusion and to identify communication anomalies. Deep learning method for network security is combined with an attention mechanism and bidirectional long short-term memory (Bi-LSTM) network. Frequent pattern stream characteristics were retrieved using CNN network and weights in each channel are changed. Hyperparameter tuning techniques like improvised elephant herding optimization algorithm is employed to increase the Bi-LSTM’s efficiency. The training process, the number of hidden layers and epochs are chosen optimally using these strategies. The RSOA-IEHO-BiLSTM model's effectiveness testing showed that it outperformed the competing techniques. With NSLKDD dataset model, an accuracy of 98.36% and an F-measure of 99.05% were achieved both of which point to an increase in the accuracy of malicious node detection.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Easwari Engineering College, Ramapuram, Chennai, 89, India

    B. Padmavathi, A. Bhagyalakshmi, D. Kavitha & P. Indumathy

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  1. B. Padmavathi
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  2. A. Bhagyalakshmi
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  3. D. Kavitha
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  4. P. Indumathy
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BP prepared the manuscript, AB prepared results and discussion, DK and PI reviewed the manuscript.

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Correspondence to B. Padmavathi.

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Padmavathi, B., Bhagyalakshmi, A., Kavitha, D. et al. An optimized Bi-LSTM with random synthetic over-sampling strategy for network intrusion detection. Soft Comput 28, 777–790 (2024). https://doi.org/10.1007/s00500-023-09483-0

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