Journal of Machine and Computing


Enhancing Network Security Intrusion Detection and Real-Time Response With Long Short-Term Memory Networks



Journal of Machine and Computing

Received On : 11 October 2024

Revised On : 26 January 2025

Accepted On : 02 March 2025

Published On : 05 April 2025

Volume 05, Issue 02

Pages : 994-1006


Abstract


In cybersecurity, network security systems (NSS) are technologies used to protect sensitive data against increasing cyber-attacks. This paper has carried out the process of integrating advanced Machine Learning (ML) techniques such as the Long Short-Term Memory (LSTM) networks along with the Convolutional Neural Networks (CNN) for the task of enhancing the Intrusion Detection Systems (IDS) in the NSS. Usually, the traditional models have faced many challenges, such as high false positive rates (FPR) and the need for real-time processing of extensive data streams, which make these systems insufficient to handle such scenarios. So, to handle these complications, ML has evolved to propose improvements in IDS implementation by adapting to new attacks and detecting complex patterns with greater accuracy. The study introduced a novel deep learning (DL), “GC-SLSTM,” that combined models such as Gated CNN with Stacked LSTM to address these challenges. This model includes CNN robust spatial pattern recognition and the LSTM that effectively handles the temporal data analysis. The proposed model was experimented with using the CICIDS2018 dataset, and the results demonstrate that the proposed Gated CNN + Stacked LSTM (GC-SLSTM) had achieved an accuracy of up to 99.59%, precision of 99.58% and a recall of 99.47%, culminating in an F1-score of 99.59%.


Keywords


Network Security Systems, Intrusion Detection Systems, CNN, LSTM, False Positive Rates, Machine Learning.


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CRediT Author Statement


The authors confirm contribution to the paper as follows:

Conceptualization: Rukmani Devi S, Manju A, Lakshmi T K, Venkataramanaiah B, Sureshkumar Chandrasekaran and Lakshmi Prasanna P; Methodology: Rukmani Devi S, Manju A, Lakshmi T K and Venkataramanaiah B; Software: Sureshkumar Chandrasekaran and Lakshmi Prasanna P; Data Curation: Rukmani Devi S, Manju A, Lakshmi T K and Venkataramanaiah B; Writing- Original Draft Preparation: Rukmani Devi S, Manju A, Lakshmi T K, Venkataramanaiah B, Sureshkumar Chandrasekaran and Lakshmi Prasanna P; Supervision: Rukmani Devi S, Manju A and Lakshmi T K; Validation: Sureshkumar Chandrasekaran and Lakshmi Prasanna P; Writing- Reviewing and Editing: Rukmani Devi S, Manju A, Lakshmi T K, Venkataramanaiah B, Sureshkumar Chandrasekaran and Lakshmi Prasanna P; All authors reviewed the results and approved the final version of the manuscript.


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We would like to thank Reviewers for taking the time and effort necessary to review the manuscript. We sincerely appreciate all valuable comments and suggestions, which helped us to improve the quality of the manuscript.


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Cite this article


Rukmani Devi S, Manju A, Lakshmi T K, Venkataramanaiah B, Sureshkumar Chandrasekaran and Lakshmi Prasanna P, “Enhancing Network Security Intrusion Detection and Real-Time Response with Long Short-Term Memory Networks”, Journal of Machine and Computing, pp. 994-1006, April 2025, doi: 10.53759/7669/jmc202505079.


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© 2025 Rukmani Devi S, Manju A, Lakshmi T K, Venkataramanaiah B, Sureshkumar Chandrasekaran and Lakshmi Prasanna P. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.