Machine Learning-Based Anomaly Detection for Cyber Threat Prevention
Norun Nabi1, Mohammad Mizanur Rahman2, Suday Kumer Ghosh3, Sanjida Alam4, Rony Barua5, Md. Asaduzzaman6, Nahid Reza Shatu7
Journal of Primeasia 6(1) 1-8 https://doi.org/10.25163/primeasia.6110172
Submitted: 07 January 2025 Revised: 20 March 2025 Published: 24 March 2025
Abstract
This study investigates the effectiveness of machine learning-based anomaly detection systems for cyber threat prevention, employing a quantitative research design with primary data collected from 400 cybersecurity professionals, IT administrators, and network security experts. The study investigated the implementation, efficacy, and constraints of machine learning (ML) algorithms by means of statistical examination, regression analysis, and the utilization of methodologies including decision trees, random forests, and support vector machines. A structured questionnaire was used to gather primary data for the study and sample size was 400. Findings reveal that 68% of organizations utilize ML for threat detection, with the financial sector leading at 75%. Despite promising adoption rates, challenges such as high false positive rates (54%), zero-day threat detection difficulties (41%), and data imbalance (60%) persist. Real-time learning and better integration with security infrastructure were highlighted as crucial for improving threat detection accuracy and system adaptability. While 72% of respondents viewed ML as effective, most emphasized the need for enhanced interoperability, false positive management, and incident response automation. These revelations highlight the progressive function of machine learning within the cybersecurity domain and the critical necessity for ongoing system enhancement in anticipation of forthcoming threat environments.
Keywords: Machine Learning, Anomaly Detection, Threat Prevention, Zero-Day Threats, Security Integration, Incident Response Automation.
References
Ali, T., & Kostakos, P. (2023). Huntgpt: Integrating machine learning-based anomaly detection and explainable ai with large language models (llms). ArXiv Preprint ArXiv:2309.16021.
Duong, H.-T., Le, V.-T., & Hoang, V. T. (2023). Deep learning-based anomaly detection in video surveillance: A survey. Sensors, 23(11), 5024.
Garcia, J. F. C., & Blandon, G. E. T. (2022). A deep learning-based intrusion detection and preventation system for detecting and preventing denial-of-service attacks. IEEE Access, 10, 83043–83060.
Goswami, M. (2024). AI-based anomaly detection for real-time cybersecurity. International Journal of Research and Review Techniques, 3(1), 45–53.
Hdaib, M., Rajasegarar, S., & Pan, L. (2024). Quantum deep learning-based anomaly detection for enhanced network security. Quantum Machine Intelligence, 6(1), 26.
Ijiga, O. M., Idoko, I. P., Ebiega, G. I., Olajide, F. I., Olatunde, T. I., & Ukaegbu, C. (2024). Harnessing adversarial machine learning for advanced threat detection: AI-driven strategies in cybersecurity risk assessment and fraud prevention. J. Sci. Technol, 11, 1–24.
Inuwa, M. M., & Das, R. (2024). A comparative analysis of various machine learning methods for anomaly detection in cyber attacks on IoT networks. Internet of Things, 26, 101162.
Jadidi, Z., Pal, S., Nayak, N., Selvakkumar, A., Chang, C.-C., Beheshti, M., & Jolfaei, A. (2022). Security of machine learning-based anomaly detection in cyber physical systems. 2022 International Conference on Computer Communications and Networks (ICCCN), 1–7.
Jayasinghe, S., Siriwardhana, Y., Porambage, P., Liyanage, M., & Ylianttila, M. (2022). Federated learning based anomaly detection as an enabler for securing network and service management automation in beyond 5g networks. 2022 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), 345–350.
Khayyat, M. M. (2023). Improved bacterial foraging optimization with deep learning based anomaly detection in smart cities. Alexandria Engineering Journal, 75, 407–417.
Lutsiv, N., Maksymyuk, T., Beshley, M., Lavriv, O., Andrushchak, V., Sachenko, A., Vokorokos, L., & Gazda, J. (2022). Deep Semisupervised Learning-Based Network Anomaly Detection in Heterogeneous Information Systems. Computers, Materials & Continua, 70(1).
Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021). Machine learning for anomaly detection: A systematic review. Ieee Access, 9, 78658–78700.
Okoli, U. I., Obi, O. C., Adewusi, A. O., & Abrahams, T. O. (2024). Machine learning in cybersecurity: A review of threat detection and defense mechanisms. World Journal of Advanced Research and Reviews, 21(1), 2286–2295.
Shah, V. (2021). Machine learning algorithms for cybersecurity: Detecting and preventing threats. Revista Espanola de Documentacion Cientifica, 15(4), 42–66.
Ullah, I., & Mahmoud, Q. H. (2021). Design and development of a deep learning-based model for anomaly detection in IoT networks. IEEe Access, 9, 103906–103926.
Wang, S., Jiang, R., Wang, Z., & Zhou, Y. (2024). Deep learning-based anomaly detection and log analysis for computer networks. ArXiv Preprint ArXiv:2407.05639.
Yaqoob, S., Hussain, A., Subhan, F., Pappalardo, G., & Awais, M. (2023). Deep learning-based anomaly detection for fog-assisted IoVs network. IEEE Access, 11, 19024–19038.
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