International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Machine Learning Techniques in Intelligent Threat Detection and Prevention of Cyber Attacks |
|---|---|
| ABSTRACT | Nevertheless, the increasing complexity and number of cyber-attacks have created an unavoidable need to move away from traditional signature-based security systems to intelligent adaptive security systems. This research paper proposes an in-depth analysis of machine learning (ML) methodologies that can be applied for the development of optimal threat detection and prevention systems in cybersecurity. Thus, through the application of existing research work and industry practices, this research paper aims to examine the potential applicability of supervised, unsupervised, and hybrid ML models for some of the most critical areas of cybersecurity, including network intrusion detection, behavioral anomaly analysis, and malware detection, through the presentation of an innovative comparative taxonomy for assessing ML models for accuracy, adaptability, computation, and adversarial attacks. Our methodological review outlines the five core implementations of ML: RF for intrusion detection, IF for unsupervised anomaly discovery, K-Means Clustering for behavioral profiling, DNNs for malware analysis, and a Twined Ensemble Model for multifunction threat mitigation. Results show that while pure algorithms such as Random Forest are successful in high-accuracy detection for known attacks, hybrid and ensemble approaches combining multiple algorithms outperform single models by leaps and bounds in the detection of zero-day exploits and sophisticated, multi-stage attacks. We provide quantitative proof that advanced ML-driven systems can reduce detection latency by over 80% and decrease breach-related costs by as much as $1.9 million compared to traditional methods. However, several challenges need consideration, which include performance reductions driven by high false-positive rates, dependence on quality data, and the emerging risk of adversarial machine learning. The conclusion places a strategic importance on ML in a layered fashion in order to advance a "human-in-the-loop" paradigm where AI enhances analyst talent to create strong, proactive defenses for a moving target. |
| AUTHOR | MANIVASAGAN. C, DR. R. BALAKRISHNAN, DR. M. SUBRAMANIAKUMAR, DR. S. JAWAHAR Assistant Professor, Department of BCA, RVS College of Arts and Science, Coimbatore, Tamil Nadu, India Associate Professor, School of Information Science, Presidency University, Karnataka, India Assistant Professor, Department of Computer Science (PG), School of computational and physical sciences, Kristu Jayanti (Deemed to be University), Bengaluru, India Assistant Professor, Department of Statistics and Data Science, CHRIST (Deemed to be University) Central Campus, Bengaluru, Karnataka, India |
| VOLUME | 181 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1402022 |
| pdf/22_Machine Learning Techniques in Intelligent Threat Detection and Prevention of Cyber Attacks.pdf | |
| KEYWORDS | |
| References | [1]. "What is the difference between supervised and unsupervised anomaly detection?," Milvus, 2023. [Online]. Available: https://milvus.io/ai-quick-reference/what-is-the-difference-between-supervised-and-unsupervised-anomaly-detection [2]. "Machine learning (ML) in cybersecurity," SailPoint, 2024. [Online]. Available: https://www.sailpoint.com/identity-library/how-ai-and-machine-learning-are-improving-cybersecurity [3]. G. Gill, "Machine Learning in Threat Detection: What Actually Works," US Cybersecurity, Dec. 2020. [Online]. Available: https://www.uscybersecurity.net/machine-learning-in-threat-detection-what-actually-works/ [4]. M. Marshall, "Anomaly Detection: How Machine Learning Is Revolutionizing Security Pattern Recognition," Avatier Blog, Oct. 21, 2025. [Online]. Available: https://www.avatier.com/blog/anomaly-detection-learning/ [5]. S. S. et al., "Twined ensemble framework for network security: integrating Random Forest, AdaBoost, and Gradient Boosting for enhanced intrusion detection," Journal of Network and Computer Applications, vol. 5, no. 107, 2025. [Online]. Available: https://link.springer.com/article/10.1007/s43926-025-00199-1 [6]. M. A. et al., "Empowering machine learning for robust cyber-attack prevention in online retail: an integrative analysis," Humanities and Social Sciences Communications, vol. 12, no. 733, 2025. [Online]. Available: https://www.nature.com/articles/s41599-025-04636-y [7]. J. D. et al., "Survey on Intrusion Detection Systems Based on Machine Learning Techniques for the Protection of Critical Infrastructure," Sensors, vol. 23, no. 5, p. 2415, 2023. [Online]. Available: https://www.mdpi.com/1424-8220/23/5/2415 [8]. B. Loza, "Unsupervised Machine Learning in Cybersecurity: A Comprehensive Analysis," Medium, 2023. [Online]. Available: https://medium.com/@leev574/unsupervised-machine-learning-in-cybersecurity-a-comprehensive-analysis-aa4d854e65d2 [9]. "Machine Learning (ML) in Cybersecurity: Use Cases," CrowdStrike, 2024. [Online]. Available: https://www.crowdstrike.com/en-us/cybersecurity-101/artificial-intelligence/machine-learning/ [10]. A. Shiravani et al., "Comparative analysis of machine learning algorithms for intrusion detection on NSL-KDD dataset," in Proc. IEEE Int. Conf. Cyber Security and Cloud Computing, 2022, pp. 145-150. [11]. Y. Mirsky et al., "Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection," in Proc. Network and Distributed Systems Security (NDSS) Symposium, 2018. [12]. N. Moustafa and J. Slay, "UNSW-NB15: a comprehensive data set for network intrusion detection systems," in Proc. Military Communications and Information Systems Conference (MilCIS), 2015, pp. 1-6. [13]. I. Sharafaldin, A. H. Lashkari, and A. A. Ghorbani, "Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization," in Proc. 4th International Conference on Information Systems Security and Privacy (ICISSP), 2018, pp. 108-116. [14]. H. H. Pajouh et al., "A Two-Layer Dimension Reduction and Two-Tier Classification Model for Anomaly-Based Intrusion Detection in IoT Backbone Networks," IEEE Transactions on Emerging Topics in Computing, vol. 7, no. 2, pp. 314-323, Apr.-Jun. 2019. [15]. W. Wang et al., "Hast-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection," IEEE Access, vol. 6, pp. 1792-1806, 2018. [16]. M. Z. Alom et al., "Intrusion Detection using Deep Belief Networks and Extreme Learning Machines," in Proc. International Conference on Computer and Information Technology (ICCIT), 2015, pp. 1-6. [17]. N. Kaloudi and J. Li, "The AI-based cyber threat landscape: A survey," ACM Computing Surveys (CSUR), vol. 53, no. 1, pp. 1-34, 2020. [18]. A. Khraisat et al., "Survey of intrusion detection systems: techniques, datasets and challenges," Cybersecurity, vol. 2, no. 1, p. 20, 2019. [19]. S. Gamage and J. Samarabandu, "Deep learning methods in network intrusion detection: A survey and an objective comparison," Journal of Network and Computer Applications, vol. 169, p. 102767, 2020. [20]. R. Vinayakumar et al., "Deep Learning Approach for Intelligent Intrusion Detection System," IEEE Access, vol. 7, pp. 41525-41550, 2019. |
