Enhancing Intrusion Detection with LSTM Recurrent Neural Network Optimized by Emperor Penguin Algorithm

Authors

DOI:

https://doi.org/10.31185/wjcms.166

Keywords:

Intrusion Detection Systems, Penguin Meta-Heuristic Algorithm, Long-Term Memory Neural Network, Linear Detection Analysis.

Abstract

Intrusion detection systems (IDS) have been developed to identify and classify these attacks in order to prevent them from occurring. However, the accuracy and efficiency of these systems are still not satisfactory. In previous research, most of the methods used were based on ordinary neural networks, which had low accuracy. Therefore, this thesis, with the aim of presenting a new approach to intrusion detection and improving its accuracy and efficiency, uses long-term memory (LSTM) optimized with the Penguin optimization algorithm (EPO). In the proposed approach, first, the features were pre-processed by normalization, cleaning, and formatting in number format. In the next step, the linear discriminant analysis (LDA) method was used to reduce the dimensions of the processed features, and after that, the EPO algorithm was used to optimize the size of the hidden unit of the LSTM network. Finally, the optimized network was evaluated using the NSL-KDD dataset, which is a widely used benchmark dataset in the field of intrusion detection. The results obtained for the training and test datasets were 99.4 and 98.8%, respectively. These results show that the proposed approach can accurately identify and classify network intrusions and outperform many existing approaches.

 

Keywords: Intrusion Detection Systems, Penguin Meta-Heuristic Algorithm, Long-Term Memory Neural Network, Linear Detection Analysis.

 

References

J. Anderson Cybersecurity Threats, Vulnerabilities, and Trends: An Overview, 2022.

H. Zhang, J. Liu, C. Wu, and J. Wang, “A Comprehensive Survey of Deep Learning in Network Anomaly Detection,” IEEE Access, vol. 10, pp. 15359–15377, 2022.

Y. Li, Y. Jin, and D. Gong, “Anomaly Detection for Industrial Control Systems Using Bidirectional LSTM with Attention Mechanism,” IEEE Transactions on Industrial Informatics, 2023.

X. Lin, X. Zheng, J. He, and D. Huang, “An Improved Emperor Penguin Optimization Algorithm for Solving Numerical Optimization Problems,” IEEE Access, vol. 9, pp. 36753–36764, 2021.

P. K. Sahu and S. Panda, “Hybridization of Emperor Penguin Algorithm with Differential Evolution for Solving Economic Load Dispatch Problem,” Swarm and Evolutionary Computation, vol. 67, pp. 100997–100997, 2022.

I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, “Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization,” IEEE Transactions on Dependable and Secure Computing, vol. 15, no. 5, pp. 849–864, 2018.

Y. Wang, C. Jia, K. Ren, and W. Lou, “Neural Network Ensembles for Intrusion Detection: A Comprehensive Survey,” IEEE Communications Surveys & Tutorials, vol. 23, no. 3, pp. 2244–2272, 2021.

F. Kuang, W. Xu, and S. Zhang, “A novel hybrid KPCA and SVM with GA model for intrusion detection,” Appl. Soft Comput, vol. 18, pp. 178–184, 2014.

R. R. Reddy, Y. Ramadevi, and K. V. N. Sunitha, “Effective discriminant function for intrusion detection using SVM,” in International Conference on Advances in Computing, Communications and Informatics (ICACCI), pp. 1148–1153, 2016.

W. Li, P. Yi, Y. Wu, L. Pan, and J. Li, “A new intrusion detection system based on KNN classification algorithm in wireless sensor network,” IEEE J. Elect. Comput. Eng, vol. 2014, no. 240217, 2014.

B. Ingre and A. Yadav, “Performance analysis of NSL-KDD dataset using ANN,” Proc. IEEE Int. Conf. Signal Process, pp. 92–96, 2015.

N. Farnaaz and M. A. Jabbar, “Random forest modeling for network intrusion detection system,” Procedia Comput. Sci, vol. 89, pp. 213–217, 2016.

J. Zhang, M. Zulkernine, and A. Haque, “Random-forests-based network intrusion detection systems,” Man, Cybern. C, Appl. Rev, vol. 38, no. 5, pp. 649–659, 2008.

J. A. Khan and N. Jain, “A survey on intrusion detection systems and classification techniques,” Int. J. Sci. Res. Sci., Eng. Technol, vol. 2, no. 5, pp. 202–208, 2016.

A. L. Buczak and E. Guven, “A survey of data mining and machine learning methods for cyber security intrusion detection,” IEEE Commun. Surveys Tuts, vol. 18, no. 2, pp. 1153–1176, 2016.

A. Javaid, Q. Niyaz, W. Sun, and M. Alam, “A deep learning approach for network intrusion detection system,” in presented at the 9th EAI Int. Conf. Bio-inspired Inf, pp. 21–26, 2016.

T. A. Tang, L. Mhamdi, D. Mclernon, S. A. R. Zaidi, and M. Ghogho, “Deep learning approach for network intrusion detection in software defined networking,” Proc. IEEE Int. Conf. Wireless Netw. Mobile Commun. (WINCOM), pp. 258–263, 2016.

M. Sheikhan, Z. Jadidi, and A. Farrokhi, “Intrusion detection using reduced-size RNN based on feature grouping,” Neural Comput. Appl, vol. 21, no. 6, pp. 1185–1190, 2012.

M. Tavallaee, E. Bagheri, W. Lu, and A. A. Ghorbani, “A detailed analysis of the KDD CUP 99 data set,” Proceedings of the 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), pp. 1–6, 2009.

C. Yin, “A deep learning approach for intrusion detection using recurrent neural networks,” IEEE Access, vol. 5, pp. 21954–21961, 2017.

S. W. Lin, “An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection,” Appl. Soft Comput, vol. 12, no. 10, pp. 3285–3290, 2012.

A. A. Aburomman and M. B. I. Reaz, “A novel SVM-kNN-PSO ensemble method for intrusion detection system,” Appl. Soft Comput, vol. 38, pp. 360–372, 2016

Downloads

Published

2023-09-30

Issue

Section

Computer

How to Cite

[1]
Saif Wali Ali Alsudani and Adel Ghazikhani, “Enhancing Intrusion Detection with LSTM Recurrent Neural Network Optimized by Emperor Penguin Algorithm”, WJCMS, vol. 2, no. 3, pp. 69–80, Sep. 2023, doi: 10.31185/wjcms.166.