A REVIEW OF THE APPLICATIONS OF MACHINE LEARNING IN CYBERSECURITY AND ITS CHALLENGES
DOI:
https://doi.org/10.29121/digisecforensics.v1.i1.2024.17Keywords:
Machine Learning, Cybersecurity, Deep Learning, Cyber-Attack, Malware Detection, Phishing URL, Network Intrusion DetectionAbstract
Our study aims to identify use of Machine Learning (ML) to solve cybersecurity problems and its challenges. ML algorithms are used for malicious network traffic detection, phishing Universal Resource Locator (URLs) detection, malware analysis, and more. Based on the review of selected articles, it is inferred that high-quality data sets, efficient feature extraction, proper training, and testing of ML mode can deliver accurate detection model. Even though the integration of ML in cybersecurity can improve threat detection capabilities, there are some challenges, including limited availability of real-world cyber datasets to train the ML models, the vulnerability of the ML model to data poisoning, imbalanced data sets causing biased detection, and data protection laws making hard to collect necessary data to train and test the ML model efficiently.
References
Bharadiya, J. (2023). Machine Learning in Cybersecurity: Techniques and Challenges. European Journal of Technology, 7(2), 1-14. https://doi.org/10.47672/ejt.1486 DOI: https://doi.org/10.47672/ejt.1486
Dasgupta, D., Akhtar, Z., & Sen, S. (2022). Machine Learning in Cybersecurity: A Comprehensive Survey. The Journal of Defense Modeling and Simulation, 19(1), 57-106. https://doi.org/10.1177/1548512920951275 DOI: https://doi.org/10.1177/1548512920951275
Rao, B. B., & Swathi, K. (2017). Fast kNN Classifiers for Network Intrusion Detection System. Indian J. Sci. Technol., 10(14), 1-10. https://doi.org/10.17485/ijst/2017/v10i14/93690 DOI: https://doi.org/10.17485/ijst/2017/v10i14/93690
Sahingoz, O. K., Buber, E., Demir, O., & Diri, B. (2019). Machine Learning Based Phishing Detection from URLs. Expert Systems with Applications, 117, 345-357. https://doi.org/10.1016/j.eswa.2018.09.029 DOI: https://doi.org/10.1016/j.eswa.2018.09.029
Ucci, D., Aniello, L., & Baldoni, R. (2019). Survey of Machine Learning Techniques for Malware Analysis. Computers & Security, 81, 123-147. https://doi.org/10.1016/j.cose.2018.11.001 DOI: https://doi.org/10.1016/j.cose.2018.11.001
Xin, Y., Kong, L., Liu, Z., Chen, Y., Li, Y., Zhu, H., & Wang, C. (2018). Machine Learning and Deep Learning Methods for Cybersecurity. Ieee Access, 6, 35365-35381. https://doi.org/10.1109/ACCESS.2018.2836950 DOI: https://doi.org/10.1109/ACCESS.2018.2836950
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Aishwarya Ulhas Desai
This work is licensed under a Creative Commons Attribution 4.0 International License.
With the licence CC-BY, authors retain the copyright, allowing anyone to download, reuse, re-print, modify, distribute, and/or copy their contribution. The work must be properly attributed to its author.
It is not necessary to ask for further permission from the author or journal board.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
