THE ROLE OF ARTIFICIAL INTELLIGENCE IN CYBERSECURITY: THREATS, DEFENSE MECHANISMS, AND FUTURE PERSPECTIVES
- Authors
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Rasulov Hasan Rustamovich
Asia International University, teacher of the "General Technical Sciences" department
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- Keywords:
- Artificial Intelligence, cybersecurity, machine learning, deep learning, threat detection, intrusion detection systems, adversarial AI, neural networks, automated security, cyber defense.
- Abstract
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This paper examines the transformative impact of artificial intelligence (AI) on cybersecurity systems and practices. As cyber threats become increasingly sophisticated and automated, traditional security measures are proving inadequate. AI-powered cybersecurity solutions offer enhanced threat detection, automated response mechanisms, and predictive analytics that significantly improve organizational security postures. This study explores the integration of machine learning, deep learning, and neural networks into cybersecurity frameworks, analyzing their applications in intrusion detection, malware analysis, phishing prevention, and behavioral analytics. The research also addresses the dual-edged nature of AI in cybersecurity, where the same technologies used for defense can be weaponized by malicious actors. Key challenges including adversarial AI, algorithmic bias, false positives, and the skills gap in AI-cybersecurity expertise are thoroughly discussed. The findings demonstrate that organizations implementing AI-driven security systems experience faster threat detection, reduced response times, and improved overall security effectiveness, though success requires careful implementation, continuous model training, and human oversight.
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- References
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1.Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
2.Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
3.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.
4.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.
5.Apruzzese, G., et al. (2022). The Role of Machine Learning in Cybersecurity. Digital Threats: Research and Practice, 3(1), 1-32.
6.Papernot, N., et al. (2018). Deep Learning-Based Security Analytics: Opportunities and Challenges. Proceedings of the IEEE Security and Privacy Workshops, 127-137.
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- Published
- 2025-11-11
- Section
- Articles
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This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.
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