A Comprehensive Investigation into Integrating Artificial Intelligence and Machine Learning for Enhanced Cybersecurity

Srinivas Rao

doi:10.36676/urr.v10.i3.1586

Authors

Srinivas Rao Computer science, Research Scholar, Kalinga University

DOI:

https://doi.org/10.36676/urr.v10.i3.1586

Keywords:

Integrating Artificial Intelligence, Machine Learning, Enhanced Cybersecurity

Abstract

People, businesses, and vital infrastructure are at serious danger from the sophisticated and persistent cyberthreats that have emerged as a result of the quickly changing digital world. Modern attack vectors including ransomware, polymorphic malware, advanced persistent threats (APTs), and zero-day vulnerabilities are outperforming traditional cybersecurity systems, which mostly depend on predetermined rules and signature-based detection. As a result, machine learning (ML) and artificial intelligence (AI) have become game-changing technologies that may improve cyber defences via predictive analytics, intelligent automation, and flexibility.

The integration of AI and ML into cybersecurity frameworks is examined in this paper, with a focus on how these technologies might improve threat prevention, detection, and response capabilities. Predictive threat modelling, which uses historical and real-time data to predict possible breaches; behavior-based anomaly detection, which spots suspicious activity outside of known attack patterns; automated incident response, which allows for quick threat containment and remediation; and proactive risk assessment, which aids in well-informed security policy decisions, are some of the main application areas. In addition to discussing cutting-edge techniques and technologies now in use, the article offers a systematic analysis of current issues, including model explainability, data quality issues, and adversarial assaults on AI systems.

References

Anderson, H. S., & Roth, P. (2018). EMBER: An open dataset for training static PE malware machine learning models. arXiv preprint arXiv:1804.04637. https://doi.org/10.48550/arXiv.1804.04637

Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. International Conference on Learning Representations. https://doi.org/10.48550/arXiv.1412.6572

Moustafa, N., & Slay, J. (2015). UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). Military Communications and Information Systems Conference (MilCIS), 1–6. IEEE. https://doi.org/10.1109/MilCIS.2015.7348942

MITRE Corporation. (2020). MITRE ATT&CK®: Design and philosophy. MITRE ATT&CK® Knowledge Base. https://attack.mitre.org

Sharafaldin, I., Lashkari, A. H., & Ghorbani, A. A. (2018). Toward generating a new intrusion detection dataset and intrusion traffic characterization. In Proceedings of the 4th International Conference on Information Systems Security and Privacy (pp. 108–116). SCITEPRESS. https://doi.org/10.5220/0006639801080116

Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J. F., & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28, 2503–2511. https://doi.org/10.48550/arXiv.1507.04296

Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. IEEE Symposium on Security and Privacy, 305–316. IEEE. https://doi.org/10.1109/SP.2010.25

Yuan, X., He, P., Zhu, Q., & Li, X. (2019). Adversarial examples: Attacks and defenses for deep learning. IEEE Transactions on Neural Networks and Learning Systems, 30(9), 2805–2824. https://doi.org/10.1109/TNNLS.2018.2886017