A Comparative Study of Artificial Intelligence and Machine Learning Algorithms for Cybersecurity


  • Sai Kiran Arcot Ramesh


Artificial Intelligence; Machine Learning; Cyber Security; Security Analysis; Risks; Threats.


The rapid expansion of cyberspace has been facilitated by a range of innovative networking and computing technologies, including software-defined networking, big data, and fog computing. Currently, cyber security has emerged as a paramount concern in the realm of cyberspace. The security of cyberspace has had significant effects on multiple essential infrastructures. The passive protection approach is no longer effective in safeguarding systems against emerging cyber risks, such as advanced persistent threats and zero-day assaults. So, the main objective of this study is to conduct a thorough examination of different implementations of artificial intelligence in the field of cybersecurity, encompassing activities such as identifying potential risks, responding to security incidents, and utilizing predictive analytics. The methodology employed in this study is qualitative research technique. The study emphasizes the efficacy of AI-powered solutions in strengthening the robustness of contemporary cybersecurity frameworks, based on current case studies and breakthroughs in machine learning algorithms. The paper critically examines the constraints and possible prejudices in AI systems used for cybersecurity, highlighting the significance of responsible AI methodologies. The study will be a contribution to the researchers, practitioners, and policymakers to know about the present condition of artificial intelligence (AI) in cybersecurity. It aims to encourage discussions on the efficient incorporation of AI technologies to tackle the continuously expanding challenges in the field of cyber threats.


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Li, J. H. (2018). Cyber security meets artificial intelligence: a survey. Frontiers of Information Technology & Electronic Engineering, 19(12), 1462-1474.

Apruzzese, G., Colajanni, M., Ferretti, L., Guido, A., & Marchetti, M. (2018, May). On the effectiveness of machine and deep learning for cyber security. In 2018 10th international conference on cyber Conflict (CyCon) (pp. 371-390). IEEE.

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.

Martínez Torres, J., Iglesias Comesaña, C., & García-Nieto, P. J. (2019). Machine learning techniques applied to cybersecurity. International Journal of Machine Learning and Cybernetics, 10(10), 2823-2836.

Halbouni, A., Gunawan, T. S., Habaebi, M. H., Halbouni, M., Kartiwi, M., & Ahmad, R. (2022). Machine learning and deep learning approaches for cybersecurity: A review. IEEE Access, 10, 19572-19585.

Husák, M., Bartoš, V., Sokol, P., & Gajdoš, A. (2021). Predictive methods in cyber defense: Current experience and research challenges. Future Generation Computer Systems, 115, 517-530.

Vemuri, N., Thaneeru, N., & Tatikonda, V. M. (2023). Securing Trust: Ethical Considerations in AI for Cybersecurity. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 2(2), 167-175.

Rangaraju, S. (2023). Secure by Intelligence: Enhancing Products with AI-Driven Security Measures. EPH-International Journal of Science And Engineering,” 9(3), 36-41.

Labu, M. R., & Ahammed, M. F. (2024). Next-Generation Cyber Threat Detection and Mitigation Strategies: A Focus on Artificial Intelligence and Machine Learning. Journal of Computer Science and Technology Studies, 6(1), 179-188.

Kasowaki, L., & Emre, B. (2024). Fortifying Cyber Defenses: Tactics for Secure Digital Environments (No. 11702). EasyChair.




How to Cite

Arcot Ramesh, S. K. (2024). A Comparative Study of Artificial Intelligence and Machine Learning Algorithms for Cybersecurity. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1165–1170. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5546



Research Article