AI and Machine Learning in Cyber Threat Detection
DOI:
https://doi.org/10.56472/ICCSAIML25-102Keywords:
Artificial Intelligence, Machine Learning, Cyber Threat Detection, Security Systems, Anomaly Detection, Cybersecurity, Deep LearningAbstract
The complexity of cyberthreats is making traditional security methods less and less effective. AI and ML are becoming powerful tools for detecting and preventing cyberattacks. With the use of vast amounts of data, AI-driven systems are able to identify trends, identify anomalies, and respond to threats with remarkable speed and accuracy. AI algorithms are constantly evolving, absorbing new threats and adjusting their defenses in real time, unlike conventional rule-based systems. This proactive approach helps organizations stay ahead of sophisticated attacks like ransomware, phishing, and zero-day exploits. AI models are particularly good at behavioral analysis, network traffic monitoring, and endpoint security Direct learning and other approaches draw awareness to known dangers while independent learning identifies patterns that could otherwise go unnoticed and indicate illegal conduct. Threat response systems can also benefit from knowledge expansion. AI has limited cybersecurity potential. Despite its potential, AI in cybersecurity has limits. Bias in training data, negative attacks designed to fool algorithms and ethical issues connected with data privacy must be correctly considered. Data privacy concerns need to be thoroughly thought out.However, as AI and ML develop further, they are transforming cybersecurity and giving companies the ability to identify risks sooner & react more quickly. Latest AI-driven techniques for cyberthreat detection are examined in this study, with a focus on real-world applications, current limitations, and future directions. By understanding how AI enhances security, businesses can better prepare to protect against a problem scenario that is always evolving
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References
[1] Pavan Kumar, P., Satish, M., Sunitha Devi, B., Prakash, A., Pradeep Reddy, K., & Malli Babu, S. (2023, December). The Future of AI in Predicting Cybersecurity Threats. In International Conference on Data Science, Machine Learning and Applications (pp. 1382-1395). Singapore: Springer Nature Singapore.
[2] Vaddadi, S. A., Vallabhaneni, R., & Whig, P. (2023). Utilizing AI and machine learning in cybersecurity for sustainable development through enhanced threat detection and mitigation. International Journal of Sustainable Development Through AI, ML and IoT, 2(2), 1-8.
[3] Mahendiran, N. (2024). CYBER THREAT DETECTION USING AI. International Journal of Multidisciplinary Research and Explorer, 4(1), 28-37.
[4] Pavan, S., Suhas, M. R., Yogesh, B., Surendra Babu, K. N., & Thirumala Akash, K. (2024, February). Intrusion Detection Landscape: Exploring Progress and Confronting Challenges in Security Advances. In 2024 International Conference on Integrated Circuits and Communication Systems (ICICACS) (pp. 1-8). IEEE.
[5] Reddy, S. P. K., Dey, N. S., SrujanGoud, A., & Rakshitha, U. (2024, June). Quantum-Inspired Machine Learning Models for Cyber Threat Intelligence. In International Conference on Intelligent Computing and Big Data Analytics (pp. 106-126). Cham: Springer Nature Switzerland.
[6] Naveen, M., & JP, M. P. S. (2024). Mrs. Ramya VJ 2 Mr. Pavan LR 3 Ms. Preethu BR 4 Ms Chandana S
[7] J. Electrical Systems, 20(10s), 6646-6653.
[8] Kumar, M. K. P., Siddhu, N., Kumar, K. S., Prasad, R., & Amarkanth, R. (2024). CMTSNN A deep learning model for multiclassification of anomalous and encrypted IoT traffic. International Journal for Innovative Engineering & Management Research, 13(4).
[9] Preethi, T., Reddy, P. R., Likhitha, L., Kumar, P. P., & Kamani, A. (2024, February). A Novel Approach for Anomaly Detection using Snort Integrated with Machine Learning. In 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 796-801). IEEE.
[10] Kasula, B. Y., & Whig, P. (2024, March). Enhancing Cybersecurity Defenses: A Comprehensive Exploration of Applied Artificial Intelligence Strategies. In International Conference on Emerging Trends and Technologies on Intelligent Systems (pp. 43-55). Singapore: Springer Nature Singapore.
[11] Om Prakash, J., Gururaj, H. L., Pooja, M. R., & Pavan Kumar, S. P. (Eds.). (2022). Methods, Implementation, and Application of Cyber Security Intelligence and Analytics. IGI Global.
[12] Mishra, T. K., Karthik, S., Teja, P. S., Vignesh, R. P., & Kumar, Y. V. (2024, April). Application of Machine Learning Algorithms and Feature Selection using Genetic Algorithm: A Case Study on Cyber Attack Detection. In 2024 IEEE 9th International Conference for Convergence in Technology (I2CT) (pp. 1-6). IEEE.
[13] Allagi, S., Pawan, T., Mainalli, K., & Dharwadkar, N. (2024, July). Leveraging AI and ML for Predictive Analysis and Feature Attribution in Abnormal Network Behavior Detection. In 2024 2nd World Conference on Communication & Computing (WCONF) (pp. 1-4). IEEE.
[14] Patil, P., Thealla, P., & Bonde, B. (2024). Harnessing AI for Enhanced Cybersecurity: Trends, Challenges, and Future Prospects. Corporate Cybersecurity in the Aviation, Tourism, and Hospitality Sector, 258-272.
[15] Shah, P., Govindarajulu, Y., Kulkarni, P., & Parmar, M. (2023, December). Exploring AI Attacks on Hardware Accelerated Targets. In 2023 IEEE 2nd International Conference on Data, Decision and Systems (ICDDS) (pp. 1-6). IEEE.
[16] Kulkarni, P. K. V., Likith, M., Haragi, A., Jayanthi, M. G., & Kannadaguli, P. (2024, November). Sentinel AI: Revolutionizing Urban Security through Intelligent Video Surveillance in Indian Metropolises. In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET) (pp. 1-6). IEEE.
[17] R. Daruvuri, "Efficient CSI feedback for large-scale MIMO IoT systems using YOLOv8-based network," in Proc. 1st IEEE Conf. Secure and Trustworthy CyberInfrastructure for IoT and Microelectronics (SaTC), Ohio, USA, 2025, pp. 1–5.
