A review on machine learning methods for intrusion detection system

Research Article
Open access

A review on machine learning methods for intrusion detection system

Man Ni 1*
  • 1 Xi’an-Jiaotong Liverpool University    
  • *corresponding author man.ni20@student.xjtlu.edu.cn
Published on 11 December 2023 | https://doi.org/10.54254/2755-2721/27/20230148
ACE Vol.27
ISSN (Print): 2755-273X
ISSN (Online): 2755-2721
ISBN (Print): 978-1-83558-199-5
ISBN (Online): 978-1-83558-200-8

Abstract

With the increasing access to the Internet and the development of information technology, concerns about computer security have been raised on a considerably large scale. Computer crimes contain various methods to undermine information privacy and system integrity, causing millions to trillions lose in the past few years. It is urgent to improve the security algorithms and models to perform as a thorough structure to prevent attacks. Among this prevention structure, an intrusion detection system (IDS) has played a vital role to monitor and detect malicious behaviours. However, due to the rapidly increasing variety of threats, the traditional algorithms are not sufficient, and new methods should be brought into IDS to improve the functionality. Deep learning (DL) and Machine learning (ML) are newly developed programs which can process data on a considerably large scale. They can also make decisions and predictions without specific programming, and these features are suitable to improve and enhance the IDS. This article mainly focuses on a review of ML methods used in IDS construction.

Keywords:

machine learning, deep learning, IDS, intrusion detection

Ni,M. (2023). A review on machine learning methods for intrusion detection system. Applied and Computational Engineering,27,57-64.
Export citation

References

[1]. ITU. (n.d.). Internet use in urban and rural areas. Retrieved March 2, 2023, from https://www.itu.int/itu-d/reports/statistics/2022/11/24/ff22-internet-use-in-urban-and-rural-areas/

[2]. Nguyen, T. (2023, January 6). A review of Cyber Crime. Retrieved March 3, 2023, from https://dzarc.com/social/article/view/244

[3]. Rao, U., & Nayak, U. (1970, January 01). Intrusion detection and prevention systems. Retrieved March 3, 2023, from https://link.springer.com/chapter/10.1007/978-1-4302-6383-8_11#Abs1

[4]. Dua, S., & Du, X. (2011). Data Mining and machine learning in Cybersecurity. Boca Raton, FL: CRC Press.

[5]. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, Perspectives, and prospects. Science, 349(6245), 255-260. doi:10.1126/science.aaa8415

[6]. Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., & Ahmad, F. (2020). Network intrusion detection system: A systematic study of machine learning and Deep Learning Approaches. Transactions on Emerging Telecommunications Technologies, 32(1). doi:10.1002/ett.4150

[7]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539

[8]. Fraley, J. B., & Cannady, J. (2017). The promise of machine learning in Cybersecurity. SoutheastCon 2017. doi:10.1109/secon.2017.7925283

[9]. Prasad, R., & Rohokale, V. (2019). Artificial Intelligence and machine learning in cyber security. Springer Series in Wireless Technology, 231-247. doi:10.1007/978-3-030-31703-4_16

[10]. Ioulianou, P., Vassilakis, V., Moscholios, I., & Logothetis, M. (2018, August 31). A signature-based intrusion detection system for the internet of things. Retrieved March 3, 2023, from https://www.ieice.org/publications/proceedings/summary.php?iconf=ICTF&session_num=SESSION02&number=SESSION02_3&year=2018

[11]. Folorunso, O., Ayo, F. E., & Babalola, Y. E. (2016). CA-NIDS: A network intrusion detection system using combinatorial algorithm approach. Journal of Information Privacy and Security, 12(4), 181-196. doi:10.1080/15536548.2016.1257680

[12]. Hamid, Y., Sugumaran, M., & Journaux, L. (2016). Machine learning techniques for intrusion detection. Proceedings of the International Conference on Informatics and Analytics. doi:10.1145/2980258.2980378

[13]. Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176. doi:10.1109/comst.2015.2494502

[14]. Purushotham, S., Meng, C., Che, Z., & Liu, Y. (2018). Benchmarking deep learning models on large healthcare datasets. Journal of Biomedical Informatics, 83, 112-134. doi: 10.1016/j.jbi.2018.04.007

[15]. Fernandez Maimo, L., Perales Gomez, A. L., Garcia Clemente, F. J., Gil Perez, M., & Martinez Perez, G. (2018). A self-adaptive deep learning-based system for anomaly detection in 5G networks. IEEE Access, 6, 7700-7712. doi:10.1109/access.2018.2803446

[16]. Song, Y., & Lu, Y. (2015, April 25). Decision tree methods: Applications for classification and prediction. Retrieved March 3, 2023, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4466856/

[17]. Sharma, H., & Kumar, S. (2016). A survey on decision tree algorithms of classification in data mining. International Journal of Science and Research (IJSR), 5(4), 2094-2097.

[18]. Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in Artificial Neural Network Applications: A survey. Heliyon, 4(11). doi:10.1016/j.heliyon.2018.e00938

[19]. Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a convolutional neural network. 2017 International Conference on Engineering and Technology (ICET). doi:10.1109/icengtechnol.2017.8308186

[20]. Meyer, D., & Wien, F. T. (2015). Support vector machines. The Interface to libsvm in package e1071, 28, 20.

[21]. Chen, W. H., Hsu, S. H., & Shen, H. P. (2005). Application of SVM and ANN for intrusion detection. Computers & Operations Research, 32(10), 2617-2634.

[22]. Schölkopf, B., Williamson, R. C., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in neural information processing systems, 12.

[23]. Shaukat, K., Luo, S., Varadharajan, V., Hameed, I. A., & Xu, M. (2020). A survey on machine learning techniques for cyber security in the last decade. IEEE Access, 8, 222310-222354. doi:10.1109/access.2020.3041951

[24]. Bouzida, Y., & Cuppens, F. (2006, September). Neural networks vs. decision trees for intrusion detection. In IEEE/IST workshop on monitoring, attack detection and mitigation (MonAM) (Vol. 28, p. 29).

[25]. Kim, D. S., & Park, J. S. (2003). Network-based intrusion detection with support Vector Machines. Information Networking, 747-756. doi:10.1007/978-3-540-45235-5_73

[26]. Elkan, C. (2000). Results of the KDD'99 classifier learning. Acm Sigkdd Explorations Newsletter, 1(2), 63-64.

[27]. Liu, H., & Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Applied Sciences, 9(20), 4396. doi:10.3390/app9204396

[28]. Cococcioni, M., Rossi, F., Ruffaldi, E., & Saponara, S. (2019). Novel arithmetics to accelerate machine learning classifiers in autonomous driving applications. 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS). doi:10.1109/icecs46596.2019.8965031

Cite this article

Ni,M. (2023). A review on machine learning methods for intrusion detection system. Applied and Computational Engineering,27,57-64.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About volume

Volume title: Proceedings of the 2023 International Conference on Software Engineering and Machine Learning

ISBN：978-1-83558-199-5(Print) / 978-1-83558-200-8(Online)
Editor：Anil Fernando, Marwan Omar
Conference website: http://www.confseml.org
Conference date: 19 April 2023
Series: Applied and Computational Engineering
Volume number: Vol.27
ISSN：2755-2721(Print) / 2755-273X(Online)

© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. Authors who publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See Open access policy for details).

References

[1]. ITU. (n.d.). Internet use in urban and rural areas. Retrieved March 2, 2023, from https://www.itu.int/itu-d/reports/statistics/2022/11/24/ff22-internet-use-in-urban-and-rural-areas/

[2]. Nguyen, T. (2023, January 6). A review of Cyber Crime. Retrieved March 3, 2023, from https://dzarc.com/social/article/view/244

[3]. Rao, U., & Nayak, U. (1970, January 01). Intrusion detection and prevention systems. Retrieved March 3, 2023, from https://link.springer.com/chapter/10.1007/978-1-4302-6383-8_11#Abs1

[4]. Dua, S., & Du, X. (2011). Data Mining and machine learning in Cybersecurity. Boca Raton, FL: CRC Press.

[5]. Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, Perspectives, and prospects. Science, 349(6245), 255-260. doi:10.1126/science.aaa8415

[6]. Ahmad, Z., Shahid Khan, A., Wai Shiang, C., Abdullah, J., & Ahmad, F. (2020). Network intrusion detection system: A systematic study of machine learning and Deep Learning Approaches. Transactions on Emerging Telecommunications Technologies, 32(1). doi:10.1002/ett.4150

[7]. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444. doi:10.1038/nature14539

[8]. Fraley, J. B., & Cannady, J. (2017). The promise of machine learning in Cybersecurity. SoutheastCon 2017. doi:10.1109/secon.2017.7925283

[9]. Prasad, R., & Rohokale, V. (2019). Artificial Intelligence and machine learning in cyber security. Springer Series in Wireless Technology, 231-247. doi:10.1007/978-3-030-31703-4_16

[10]. Ioulianou, P., Vassilakis, V., Moscholios, I., & Logothetis, M. (2018, August 31). A signature-based intrusion detection system for the internet of things. Retrieved March 3, 2023, from https://www.ieice.org/publications/proceedings/summary.php?iconf=ICTF&session_num=SESSION02&number=SESSION02_3&year=2018

[11]. Folorunso, O., Ayo, F. E., & Babalola, Y. E. (2016). CA-NIDS: A network intrusion detection system using combinatorial algorithm approach. Journal of Information Privacy and Security, 12(4), 181-196. doi:10.1080/15536548.2016.1257680

[12]. Hamid, Y., Sugumaran, M., & Journaux, L. (2016). Machine learning techniques for intrusion detection. Proceedings of the International Conference on Informatics and Analytics. doi:10.1145/2980258.2980378

[13]. Buczak, A. L., & Guven, E. (2016). A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2), 1153-1176. doi:10.1109/comst.2015.2494502

[14]. Purushotham, S., Meng, C., Che, Z., & Liu, Y. (2018). Benchmarking deep learning models on large healthcare datasets. Journal of Biomedical Informatics, 83, 112-134. doi: 10.1016/j.jbi.2018.04.007

[15]. Fernandez Maimo, L., Perales Gomez, A. L., Garcia Clemente, F. J., Gil Perez, M., & Martinez Perez, G. (2018). A self-adaptive deep learning-based system for anomaly detection in 5G networks. IEEE Access, 6, 7700-7712. doi:10.1109/access.2018.2803446

[16]. Song, Y., & Lu, Y. (2015, April 25). Decision tree methods: Applications for classification and prediction. Retrieved March 3, 2023, from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4466856/

[17]. Sharma, H., & Kumar, S. (2016). A survey on decision tree algorithms of classification in data mining. International Journal of Science and Research (IJSR), 5(4), 2094-2097.

[18]. Abiodun, O. I., Jantan, A., Omolara, A. E., Dada, K. V., Mohamed, N. A., & Arshad, H. (2018). State-of-the-art in Artificial Neural Network Applications: A survey. Heliyon, 4(11). doi:10.1016/j.heliyon.2018.e00938

[19]. Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017). Understanding of a convolutional neural network. 2017 International Conference on Engineering and Technology (ICET). doi:10.1109/icengtechnol.2017.8308186

[20]. Meyer, D., & Wien, F. T. (2015). Support vector machines. The Interface to libsvm in package e1071, 28, 20.

[21]. Chen, W. H., Hsu, S. H., & Shen, H. P. (2005). Application of SVM and ANN for intrusion detection. Computers & Operations Research, 32(10), 2617-2634.

[22]. Schölkopf, B., Williamson, R. C., Smola, A., Shawe-Taylor, J., & Platt, J. (1999). Support vector method for novelty detection. Advances in neural information processing systems, 12.

[23]. Shaukat, K., Luo, S., Varadharajan, V., Hameed, I. A., & Xu, M. (2020). A survey on machine learning techniques for cyber security in the last decade. IEEE Access, 8, 222310-222354. doi:10.1109/access.2020.3041951

[24]. Bouzida, Y., & Cuppens, F. (2006, September). Neural networks vs. decision trees for intrusion detection. In IEEE/IST workshop on monitoring, attack detection and mitigation (MonAM) (Vol. 28, p. 29).

[25]. Kim, D. S., & Park, J. S. (2003). Network-based intrusion detection with support Vector Machines. Information Networking, 747-756. doi:10.1007/978-3-540-45235-5_73

[26]. Elkan, C. (2000). Results of the KDD'99 classifier learning. Acm Sigkdd Explorations Newsletter, 1(2), 63-64.

[27]. Liu, H., & Lang, B. (2019). Machine learning and deep learning methods for intrusion detection systems: A survey. Applied Sciences, 9(20), 4396. doi:10.3390/app9204396

[28]. Cococcioni, M., Rossi, F., Ruffaldi, E., & Saponara, S. (2019). Novel arithmetics to accelerate machine learning classifiers in autonomous driving applications. 2019 26th IEEE International Conference on Electronics, Circuits and Systems (ICECS). doi:10.1109/icecs46596.2019.8965031

For authors
Guide for authors Policies for authors Aims and scope Subscription Open access policy
For editors
Policies for editors Editorial board
For reviewers
Policies for reviewers Privacy policy

Cookies are used by this site.
Copyright © 2024 EWA Publishing, its licensors, and contributors. All rights reserved, including text and data mining, AI training, and similar technologies.