AI-Driven Cybersecurity Strategies: Leveraging Machine Learning for Threat Detection and Risk Mitigation
DOI:
https://doi.org/10.64137/XXXXXXXX/IJFEMS-V1I1P101Keywords:
AI-driven cybersecurity, Machine learning, Threat detection, Risk mitigation, Anomaly detection, Predictive analytics, Cybersecurity automation, Data preprocessing, Federated learning, Threat intelligenceAbstract
Traditional techniques used for threat detection and threat management are not strong enough to cope with the fast-changing world of cyber threats. With the help of Artificial Intelligence (AI) and Machine Learning (ML), it is now possible to strengthen cybersecurity strategies. This document explains how AI and ML are being utilised in cybersecurity for both threat detection and risk mitigation. We look at the main theories, useful practices, and possible future trends in AI-powered cybersecurity. The document presents several case studies, algorithms, and statistical information to demonstrate how these technologies operate in the real world. Ultimately, we cover the obstacles and ethical topics relevant to AI in cybersecurity
References
[1] Mbah, G. O., & Evelyn, A. N. (2024). AI-powered cybersecurity: Strategic approaches to mitigate risk and safeguard data privacy.
[2] Apruzzese, G., Laskov, P., Montes de Oca, E., Mallouli, W., Brdalo Rapa, L., Grammatopoulos, A. V., & Di Franco, F. (2023). The role of machine learning in cybersecurity. Digital Threats: Research and Practice, 4(1), 1-38.
[3] How Machine Learning Enhances Threat Detection and Response in Cybersecurity, hashstudioz, online. https://www.hashstudioz.com/blog/how-machine-learning-enhances-threat-detection-and-response-in-cybersecurity/
[4] AL-Dosari, K., Fetais, N., & Kucukvar, M. (2024). Artificial intelligence and cyber defense system for banking industry: A qualitative study of AI applications and challenges. Cybernetics and systems, 55(2), 302-330.
[5] Machine Learning in Cybersecurity: Benefits and Challenges, sangfor, 2024. online. https://www.sangfor.com/blog/cybersecurity/machine-learning-in-cybersecurity-benefits-and-challenges
[6] AI in Cybersecurity Challenges: Protect Your Business Now, devoteam, online. https://www.devoteam.com/expert-view/dangers-and-challenges-of-ai-in-cybersecurity/
[7] Muppalaneni, R., Inaganti, A. C., & Ravichandran, N. (2024). AI-Driven Threat Intelligence: Enhancing Cyber Defense with Machine Learning. Journal of Computing Innovations and Applications, 2(1), 1-11.
[8] Sundaramurthy, S. K., Ravichandran, N., Inaganti, A. C., & Muppalaneni, R. (2025). AI-Driven Threat Detection: Leveraging Machine Learning for Real-Time Cybersecurity in Cloud Environments. Artificial Intelligence and Machine Learning Review, 6(1), 23-43.
[9] Kavitha, D., & Thejas, S. (2024). Ai enabled threat detection: Leveraging artificial intelligence for advanced security and cyber threat mitigation. IEEE Access.
[10] Kühl, N., Schemmer, M., Goutier, M., & Satzger, G. (2022). Artificial intelligence and machine learning. Electronic Markets, 32(4), 2235-2244.
[11] Kamoun, F., Iqbal, F., Esseghir, M. A., & Baker, T. (2020, October). AI and machine learning: A mixed blessing for cybersecurity. In 2020 International Symposium on Networks, Computers and Communications (ISNCC) (pp. 1-7). IEEE.
[12] Biermann, E., Cloete, E., & Venter, L. M. (2001). A comparison of intrusion detection systems. Computers & Security, 20(8), 676-683.
[13] Khraisat, A., Gondal, I., Vamplew, P., & Kamruzzaman, J. (2019). Survey of intrusion detection systems: techniques, datasets and challenges. Cybersecurity, 2(1), 1-22.
[14] Lei, Y. (2017, October). Network anomaly traffic detection algorithm based on SVM. In 2017 International Conference on Robots & Intelligent System (ICRIS) (pp. 217-220). IEEE.
[15] Rathore, H., Agarwal, S., Sahay, S. K., & Sewak, M. (2018, November). Malware detection using machine learning and deep learning. In International Conference on Big Data Analytics (pp. 402-411). Cham: Springer International Publishing.
[16] Xue, L., & Luan, W. (2015, August). Improved K-means algorithm in user behavior analysis. In 2015 Ninth International Conference on Frontier of Computer Science and Technology (pp. 339-342). IEEE.
[17] Mishra, S., Albarakati, A., & Sharma, S. K. (2022). Cyber threat intelligence for IoT using machine learning. Processes, 10(12), 2673.
[18] Angelov, P., Gu, X., Kangin, D., & Principe, J. (2016, October). Empirical data analysis: A new tool for data analytics. In 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 000052-000059). IEEE.
[19] Dr. Priya. A., Dr. Charles Arockiasamy J., “The Global Reach of AI: A Postcolonial Analysis of Technological Dominance,” International Journal of Scientific Research in Science and Technology, 11(2), 1-5, 2025.
