A hybrid CNN-LSTM approach for intelligent cyber intrusion detection system

IF 4.8 2区计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS

Computers & Security Pub Date : 2024-10-09 DOI:10.1016/j.cose.2024.104146

Sukhvinder Singh Bamber , Aditya Vardhan Reddy Katkuri , Shubham Sharma , Mohit Angurala

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引用次数: 0

Abstract

As the technology is advancing more and more in the era of increasing digitalization, safeguarding networks from cyber threats is crucial. As cyber-attacks on critical infrastructure are becoming more and more sophisticated, enhancing cyber intrusion detection systems (IDS) is imperative. This paper proposes and evaluates a deep learning-based IDS using the NSL-KDD dataset, a benchmark for intrusion detection. The system pre-processes data with Recursive Feature Elimination (RFE) and a Decision Tree classifier to identify the most significant features, optimizing model performance. Various deep learning models, including ANN, LSTM, BiLSTM, CNN-LSTM, GRU, and BiGRU, have been evaluated. The CNN-LSTM model outperformed the others, with 95 % accuracy, 0.89 recall, and 0.94 f1-score. These results prove the effectiveness of the proposed IDS in accurately distinguishing between malicious and benign network traffic. Future research can explore ensemble techniques like boosting or bagging to further enhance IDS performance.

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用于智能网络入侵检测系统的混合 CNN-LSTM 方法

在日益数字化的时代，技术发展日新月异，保护网络免受网络威胁至关重要。随着对关键基础设施的网络攻击越来越复杂，加强网络入侵检测系统（IDS）势在必行。本文利用入侵检测的基准数据集 NSL-KDD，提出并评估了一种基于深度学习的 IDS。该系统利用递归特征消除（RFE）和决策树分类器对数据进行预处理，以识别最重要的特征，优化模型性能。对各种深度学习模型进行了评估，包括 ANN、LSTM、BiLSTM、CNN-LSTM、GRU 和 BiGRU。CNN-LSTM 模型的准确率为 95%，召回率为 0.89，f1 分数为 0.94，表现优于其他模型。这些结果证明了所提出的 IDS 在准确区分恶意和良性网络流量方面的有效性。未来的研究可以探索提升或装袋等集合技术，以进一步提高 IDS 性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computers & Security 工程技术-计算机：信息系统

CiteScore

12.40

自引率

7.10%

发文量

365

审稿时长

10.7 months

期刊介绍： Computers & Security is the most respected technical journal in the IT security field. With its high-profile editorial board and informative regular features and columns, the journal is essential reading for IT security professionals around the world. Computers & Security provides you with a unique blend of leading edge research and sound practical management advice. It is aimed at the professional involved with computer security, audit, control and data integrity in all sectors - industry, commerce and academia. Recognized worldwide as THE primary source of reference for applied research and technical expertise it is your first step to fully secure systems.