Artificial Intelligence for Cybersecurity: A Comprehensive Analysis of Algorithms, Frameworks, and Real-World Applications
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Saidamin Sajid*
Eid Mohammad Ibrahimi
Baryali Raoufi
The rapid rise in cyber threats has necessitated the integration of Artificial Intelligence (AI) to enhance cybersecurity strategies. This study aims to examine the effectiveness of AI algorithms in detecting and mitigating cyber threats, analyze AI-driven frameworks for cybersecurity operations, and assess real-world applications and challenges in deployment. A qualitative methodology was employed through a systematic literature review of 30 peer-reviewed articles published between 2021 and 2025, sourced from academic databases such as IEEE Xplore, ScienceDirect, Springer, and Wiley Online Library. Data extraction and screening were guided by the PRISMA protocol to ensure the inclusion of high-quality, relevant studies. Results indicate that AI techniques such as neural networks, support vector machines, and deep learning are highly effective in identifying anomalies, detecting intrusions, and analyzing malware. Furthermore, AI-based cybersecurity architectures are increasingly adaptive, scalable, and integrated with real-time response systems. However, challenges remain in model explainability, data privacy, and adversarial attacks.The study concludes that while AI significantly improves cybersecurity capabilities, its deployment must be guided by ethical, legal, and operational considerations. Future research should focus on improving model transparency and developing adaptive defense mechanisms.
Achuthan, K., Ramanathan, S., Srinivas, S., & Raman, R. (2024). Advancing cybersecurity and privacy with artificial intelligence: current trends and future research directions. Frontiers in Big Data, 7, 1497535.
Ajala, O. A., Okoye, C. C., Ofodile, O. C., Arinze, C. A., & Daraojimba, O. D. (2024). Review of AI and machine learning applications to predict and Thwart cyber-attacks in real-time. Magna Scientia Advanced Research and Reviews, 10(1), 312-320.
Berghout, T., Benbouzid, M., & Muyeen, S. M. (2022). Machine learning for cybersecurity in smart grids: A comprehensive review-based study on methods, solutions, and prospects. International Journal of Critical Infrastructure Protection, 38, 100547.
Camacho, N. G. (2024). The role of AI in cybersecurity: Addressing threats in the digital age. Journal of Artificial Intelligence General science (JAIGS) ISSN: 3006-4023, 3(1), 143-154.
Charmet, F., Tanuwidjaja, H. C., Ayoubi, S., Gimenez, P. F., Han, Y., Jmila, H., ... & Zhang, Z. (2022). Explainable artificial intelligence for cybersecurity: a literature survey. Annals of Telecommunications, 77(11), 789-812.
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.
Geetha, R., & Thilagam, T. (2021). A review on the effectiveness of machine learning and deep learning algorithms for cyber security. Archives of Computational Methods in Engineering, 28(4), 2861-2879.
Goswami, S. S., Mondal, S., Halder, R., Nayak, J., & Sil, A. (2024). Exploring the impact of artificial intelligence integration on cybersecurity: A comprehensive analysis. Journal of industrial intelligence, 2(2), 73-93.
Kamruzzaman, M., Bhuyan, M. K., Hasan, R., Farabi, S. F., Nilima, S. I., & Hossain, M. A. (2024, October). Exploring the Landscape: A Systematic Review of Artificial Intelligence Techniques in Cybersecurity. In 2024 International Conference on Communications, Computing, Cybersecurity, and Informatics (CCCI) (pp. 01-06). IEEE.
Kaur, R., Gabrijelčič, D., & Klobučar, T. (2023). Artificial intelligence for cybersecurity: Literature review and future research directions. Information Fusion, 97, 101804.
Malatji, M., & Tolah, A. (2024). Artificial intelligence (AI) cybersecurity dimensions: a comprehensive framework for understanding adversarial and offensive AI. AI and Ethics, 1-28.
Naik, B., Mehta, A., Yagnik, H., & Shah, M. (2022). The impacts of artificial intelligence techniques in augmentation of cybersecurity: a comprehensive review. Complex & Intelligent Systems, 8(2), 1763-1780.
Ofusori, L., Bokaba, T., & Mhlongo, S. (2024). Artificial Intelligence in Cybersecurity: A Comprehensive Review and Future Direction. Applied Artificial Intelligence, 38(1), 2439609.
Ozkan-Okay, M., Akin, E., Aslan, Ö., Kosunalp, S., Iliev, T., Stoyanov, I., & Beloev, I. (2024). A comprehensive survey: Evaluating the efficiency of artificial intelligence and machine learning techniques on cyber security solutions. IEEe Access, 12, 12229-12256.
Saleh, A. M. S. (2024). Blockchain for secure and decentralized artificial intelligence in cybersecurity: A comprehensive review. Blockchain: Research and Applications, 100193.
Salem, A. H., Azzam, S. M., Emam, O. E., & Abohany, A. A. (2024). Advancing cybersecurity: a comprehensive review of AI-driven detection techniques. Journal of Big Data, 11(1), 105.
Sarker, I. H. (2021a). Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN computer science, 2(6), 1-20.
Sarker, I. H. (2021b). Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), 160.
Sarker, I. H. (2023). Multi‐aspects AI‐based modeling and adversarial learning for cybersecurity intelligence and robustness: A comprehensive overview. Security and Privacy, 6(5), e295.
Sarker, I. H. (2024). CyberAI: A Comprehensive Summary of AI Variants, Explainable and Responsible AI for Cybersecurity. In AI-Driven Cybersecurity and Threat Intelligence: Cyber Automation, Intelligent Decision-Making and Explainability (pp. 173-200). Cham: Springer Nature Switzerland.
Sudaryono, S., Pratomo, R., Ramadan, A., Ahsanitaqwim, R., & Fletcher, E. (2025). Artificial Intelligence in Predictive Cybersecurity: Developing Adaptive Algorithms to Combat Emerging Threats. Journal of Computer Science and Technology Application, 2(1), 1-13.
Zhang, Z., Al Hamadi, H., Damiani, E., Yeun, C. Y., & Taher, F. (2022). Explainable artificial intelligence applications in cyber security: State-of-the-art in research. IEEe Access, 10, 93104-93139.
