Optimized Deep Learning with Binary PSO for Intrusion Detection on CSE-CIC-IDS2018 Dataset
DOI:
https://doi.org/10.29304/jqcm.2020.12.3.706Keywords:
Network Intrusion Detection System, Feature Selection, Deep Learning, Binary PSO, CSE-CIC-IDS2018Abstract
Anomaly detection is a term refer to any abnormal behaviors, comprise security breaches of network. Deep Learning (DL)has proven its outperformance compared to machine learning algorithms in solving the complex problems of real-world like intrusion detection. Though, this approach need more computational resources and consumes long time. Feature selection is play significant role of choosing the best features that describes the target concept optimally during a classification process. However, when handle large number of features the selecting of such relevant features becomes a difficult task. Thus, this paper proposes using Binary Particle Swarm Optimization (BPSO) to solve the feature selection problem. Then, features selected from BPSO are evaluated on Deep Neural Networks (DNN) classifiers and the CSE-CIC-IDS2018 dataset. The result of the proposed model has shown comparable performance based on processing time, detection rate and false alarm rate comparing with other benchmark classifiers. Experimental results have shown a high accuracy of 95%.
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References
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[22] Seyed Mojtaba Hosseini Bamakan et al., "A New Intrusion Detection Approach using PSO based Multiple Criteria Linear Programming ", Elsevier, Procedia Computer Science 55(2015) 231 – 237.
[23] D. Asir Antony Gnana Singh et al.," Enhancing the Performance of Classifier Using Particle SwarmOptimization (PSO) - based Dimensionality Reduction",International Journal of Energy, Information and Communications,Vol.6, Issue 5 (2015), pp.19-26,http://dx.doi.org/10.14257/ijeic.2015.6.5.03.
[24] H. Nezamabadi-pour, M. Rostami-shahrbabaki, M.M. Farsangi, “Binary Particle Swarm Optimization: challenges and New Solutions”, The Journal of Computer Society of Iran (CSI) On Computer Science and Engineering (JCSE), vol. 6, no. (1-A), pp. 21-32, 2008.
[25] Samaneh Mahdavifar, Ali A. Ghorbani, " Application of deep learning to cybersecurity: A survey", Elsevier, Neuro computing 347, pp. 149–176, 2019.
[26] Mehdi Mohammadi et al., " Deep Learning for IoT Big Data and Streaming Analytics: A Survey", IEEE Communications Surveys & Tutorials,2018, doi:10.1109/comst.2018.2844341.
[27] Rawaa Ismael F., Abeer T. M., Nidaa F. H.," Performance Analysis of Flow-Based Attacks Detection on CSE-CIC-IDS2018 Dataset Using Deep Learning", Indonesian Journal of Electrical Engineering and Computer Science, Vol20, No.3,2020.
[2] Bruno Bogaz Zarpelao, et al., "A Survey of Intrusion Detection in Internet of Things, Journal of Network and Computer Applications, http://dx.doi.org/10.1016/j.jnca.2017.02.009
[3] Hongyu Liu and Bo Lang, "Machine Learning and Deep Learning Methods for Intrusion Detection Systems: A Survey", applied science,2019,9,439, DOI:10.3390/app9204396
[4] Shuqiang Lu, et al., New Era of Deep learning -Based Malware Intrusion Detection: The Malware Detection and Prediction Based On Deep Learning" ,2019, ArXiv, abs/1907.08356.
[5] Ralf C. Staudemeyer,"Applying long short-term memory recurrent neural networks to Intrusion detection", SACJ, No. 56, July 2015.
[6] Navaporn Chockwanich, VasakaVisoottiviseth, "Intrusion Detection by Deep Learning with TensorFlow", International Conference on Advanced Communications (ICACT),2019.
[7] Y. Hao et al., "Variant-Gated Recurrent Units with Encoders to Preprocess Packets for Payload-Aware Intrusion Detection", IEEE, VOLUME 7, 2019.
[8] Chaopeng Li et al., "Using a Recurrent Neural Network and Restricted Boltzmann Machines for Malicious Traffic Detection", Neuro Quantology, Volume 16, Issue 5 | Page 823-831, 2018, DOI: 10.14704/nq.2018.16.5.1391
[9] Antonia Nisioti, Alexios Mylonas, Paul D. Yoo, Vasilios Katos, "From Intrusion Detection to Attacker Attribution: A Comprehensive Survey of Unsupervised Methods", DOI: 10.1109/COMST.2018.2854724, IEEE.
[10] Murooj Khalid Ibraheem, et al., " Network Intrusion Detection Using Deep Learning Based On Dimensionality Reduction", REVISTA AUS 26-2, DOI:10.4206/ Aus. 2019.n26.2.23.
[11] Constantinos Kolias,Vasilis Kolias, Georgios Kambourakis,"TermID : a distributed swarm intelligence-based approach for wireless intrusion detection", Int. J. Inf. Secur.,Springer, 2016,DOI:10.1007/s10207-016-0335-z.
[12] Mujahid H. Khalifa et al., "Particle Swarm Optimization for Deep learning of Convolution Neural Network ", Sudan Conference on Computer Science and Information Technology (SCCSIT), IEEE, 2017.
[13] Grega Vrbančič, Iztok Fister Jr. and Vili Podgorelec,"Swarm Intelligence Approaches for Parameter Setting of Deep Learning Neural Network: Case Study on Phishing Websites Classification", International Conference on Web Intelligence, Mining and Semantics, 2018, Novi Sad, Serbia. ACM, https://doi.org/10.1145/3227609.3227655
[14] Peng Wei, et al., "An Optimization Method for Intrusion Detection Classification Model based on Deep Belief Network", IEEE, doi:10.1109/ACCESS.2019.2925828.
[15] Omar Almomani, "A Feature Selection Model for Network Intrusion Detection System Based on PSO, GWO, FFA and GA Algorithms", Symmetry 2020, 12, 1046; doi:10.3390/sym12061046.
[16] Wisam Elmasry, Akhan Akbulut, Abdul Halim Zaim, "Evolving deep learning architectures for network intrusion detection using a double PSO metaheuristic", Computer Networks, 2019,doi:https://doi.org/10.1016/j.comnet.2019.107042 .
[17] Arif. J. Malik, W. Shahzad and F. A. Khan, "Network intrusion detection using hybrid binary PSOand random forests algorithm", Security An Communication Networks
Security Comm. Networks 2015; 8:2646–2660, DOI: 10.1002/sec.508.
[18] Sharafaldin, I., Lashkari, A. and Ghorbani, A.," Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization", Proceedings of the 4th International Conference on Information Systems Security and Privacy (ICISSP 2018), pages 108-116, DOI: 10.5220/0006639801080116.
[19] I. Sharafaldin et al.," Towards a Reliable Intrusion Detection Benchmark Dataset ", Journal of Software Networking, 177–200, doi: 10.13052/jsn2445-9739.2017.009.
[20] https://www.unb.ca/cic/datasets/ids-2018.html
[21] Nilesh Kunhare , Ritu Tiwari And Joydip Dhar,"Particle swarm optimization and feature selection for intrusion detection system", Sadhana (2020) 45:109 , https://doi.org/10.1007/s12046-020-1308-5Sadhana(0123456789).
[22] Seyed Mojtaba Hosseini Bamakan et al., "A New Intrusion Detection Approach using PSO based Multiple Criteria Linear Programming ", Elsevier, Procedia Computer Science 55(2015) 231 – 237.
[23] D. Asir Antony Gnana Singh et al.," Enhancing the Performance of Classifier Using Particle SwarmOptimization (PSO) - based Dimensionality Reduction",International Journal of Energy, Information and Communications,Vol.6, Issue 5 (2015), pp.19-26,http://dx.doi.org/10.14257/ijeic.2015.6.5.03.
[24] H. Nezamabadi-pour, M. Rostami-shahrbabaki, M.M. Farsangi, “Binary Particle Swarm Optimization: challenges and New Solutions”, The Journal of Computer Society of Iran (CSI) On Computer Science and Engineering (JCSE), vol. 6, no. (1-A), pp. 21-32, 2008.
[25] Samaneh Mahdavifar, Ali A. Ghorbani, " Application of deep learning to cybersecurity: A survey", Elsevier, Neuro computing 347, pp. 149–176, 2019.
[26] Mehdi Mohammadi et al., " Deep Learning for IoT Big Data and Streaming Analytics: A Survey", IEEE Communications Surveys & Tutorials,2018, doi:10.1109/comst.2018.2844341.
[27] Rawaa Ismael F., Abeer T. M., Nidaa F. H.," Performance Analysis of Flow-Based Attacks Detection on CSE-CIC-IDS2018 Dataset Using Deep Learning", Indonesian Journal of Electrical Engineering and Computer Science, Vol20, No.3,2020.
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Published
2020-11-11
How to Cite
Farhan, R. I., Maolood, A. T., & Hassan, N. F. (2020). Optimized Deep Learning with Binary PSO for Intrusion Detection on CSE-CIC-IDS2018 Dataset. Journal of Al-Qadisiyah for Computer Science and Mathematics, 12(3), Comp Page 16–27. https://doi.org/10.29304/jqcm.2020.12.3.706
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Computer Articles
