Enhancing Network Security through Machine Learning-Based Anomaly Detection Systems
Keywords:
Machine Learning, Anomaly Detection, Network Security, Data privacy and protection.Abstract
For decades, anomaly detection has been used to discover and extract aberrant components from data. Several techniques have been employed to spot irregularities. Machine learning (ML) is a method that is gaining importance due to its significant significance in this area. Machine learning models that detect anomalies in their application are the focus of this study's Systematic Literature Review (SLR). In our investigation, we look at the models from four angles: how anomaly detection is classified, what it's used for, how machine learning is done, and how well machine learning models perform. In this study, we looked for papers published in 2015–2023, which deal with the topic of anomaly detection using machine learning techniques. After we've finished analyzing the selected research papers, we'll go on to outline 10 different uses of anomaly detection that were found in those publications. The number of machine learning models used to detect anomalies is also identified, accounting for 6% of all instances. Finally, we offer available a wide range of datasets used in anomaly detection studies as well as many other generic datasets. Furthermore, compared to other categorized anomaly detection methods, researchers are more likely to employ unsupervised anomaly detection. The application of machine learning models for anomaly detection is one of the most promising fields of study, and researchers have utilized several ML models in this regard. Therefore, based on the results of this review, we advise and suggest things to researchers.
Downloads
References
Alsoufi, M. A., Razak, S., Siraj, M. M., Nafea, I., Ghaleb, F. A., Saeed, F., & Nasser, M. (2021). Anomaly-based intrusion detection systems in IoT using deep learning: A systematic literature review. Applied sciences, 11(18), 8383.
Al-Turaiki, I., & Altwaijry, N. (2021). A convolutional neural network for improved anomaly-based network intrusion detection. Big Data, 9(3), 233-252.
Bharadiya, J. (2023). Machine learning in cybersecurity: Techniques and challenges. European Journal of Technology, 7(2), 1-14.
Elmrabit, N., Zhou, F., Li, F., & Zhou, H. (2020, June). Evaluation of machine learning algorithms for anomaly detection. In 2020 international conference on cyber security and protection of digital services (cyber security) (pp. 1-8). IEEE.
Eltanbouly, S., Bashendy, M., AlNaimi, N., Chkirbene, Z., & Erbad, A. (2020, February). Machine learning techniques for network anomaly detection: A survey. In 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) (pp. 156-162). IEEE.
Fourure, D., Javaid, M. U., Posocco, N., & Tihon, S. (2021, September). Anomaly detection: how to artificially increase your f1-score with a biased evaluation protocol. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 3-18). Cham: Springer International Publishing.
Haji, S. H., & Ameen, S. Y. (2021). Attack and anomaly detection in IoT networks using machine learning techniques: A review. Asian J. Res. Comput. Sci, 9(2), 30-46.
Hossain, M. A., & Islam, M. S. (2023). Ensuring network security with a robust intrusion detection system using ensemble-based machine learning. Array, 19, 100306.
Hosseinzadeh, M., Rahmani, A. M., Vo, B., Bidaki, M., Masdari, M., & Zangakani, M. (2021). Improving security using SVM-based anomaly detection: issues and challenges. Soft Computing, 25(4), 3195-3223.
Imran, Jamil, F., & Kim, D. (2021). An ensemble of prediction and learning mechanisms for improving the accuracy of anomaly detection in network intrusion environments. Sustainability, 13(18), 10057.
Ma, X., Wu, J., Xue, S., Yang, J., Zhou, C., Sheng, Q. Z., … & Akoglu, L. (2021). A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12012-12038.
Mulinka, P., & Casas, P. (2018, August). Stream-based machine learning for network security and anomaly detection. In Proceedings of the 2018 workshop on big data analytics and machine learning for data communication networks (pp. 1-7).
Naseer, S., Saleem, Y., Khalid, S., Bashir, M. K., Han, J., Iqbal, M. M., & Han, K. (2018). Enhanced network anomaly detection based on deep neural networks. IEEE Access, 6, 48231-48246.
Pang, G., Shen, C., & Van Den Hengel, A. (2019, July). Deep anomaly detection with deviation networks. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 353-362).
Peterson, K. T., Sagan, V., & Sloan, J. J. (2020). Deep learning-based water quality estimation and anomaly detection using Landsat-8/Sentinel-2 virtual constellation and cloud computing. GIScience & Remote Sensing, 57(4), 510-525.
Poornima, I. G. A., & Paramasivan, B. (2020). Anomaly detection in wireless sensor network using a machine learning algorithm. Computer communications, 151, 331-337.
Rafi, H., Rafiq, H., & Farhan, M. (2021). Inhibition of NMDA receptors by agmatine is followed by GABA/glutamate balance in benzodiazepine withdrawal syndrome. Beni-Suef University Journal of Basic and Applied Sciences, 10(1), 1-13.
Rafi, H., Ahmad, F., Anis, J., Khan, R., Rafiq, H., & Farhan, M. (2020). Comparative effectiveness of agmatine and choline treatment in rats with cognitive impairment induced by AlCl3 and forced swim stress. Current Clinical Pharmacology, 15(3), 251-264.
Rebel, J., & Hussain, S. Machine Learning Approaches for Anomaly Detection in Network Security.
Saba, T., Rehman, A., Sadad, T., Kolivand, H., & Bahaj, S. A. (2022). Anomaly-based intrusion detection system for IoT networks through deep learning model. Computers and Electrical Engineering, 99, 107810.
Said Elsayed, M., Le-Khac, N. A., Dev, S., & Jurcut, A. D. (2020, November). Network anomaly detection using LSTM-based autoencoder. In Proceedings of the 16th ACM Symposium on QoS and Security for Wireless and Mobile Networks (pp. 37-45).
Ullah, I., & Mahmoud, Q. H. (2021). Design and development of a deep learning-based model for anomaly detection in IoT networks. IEEE Access, 9, 103906-103926.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
