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@article{199713,
        author = {Dr.M. Munafur Hussaina and Mrs. M. Nasreen Banu},
        title = {Machine Learning for Network Security and Anomaly Detection},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {15482-15491},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199713},
        abstract = {Many systems are now at risk of advanced persistent attacks, data breaches, and network invasions due to the rapid expansion of network technology and the increasing sophistication of cyber threats, which have greatly overtaken traditional security measures. Machine learning (ML) approaches have become a potent instrument in the field of anomaly detection and network security as a result of these difficulties. The use of supervised and unsupervised machine learning methods to improve network security systems is examined in this study, with a focus on intrusion detection and emerging threat mitigation. While unsupervised methods like K-Means clustering and Autoencoders are used to discover new and unknown attack patterns in real-time, supervised models like Support Vector Machines (SVM) and Random Forest are used to detect existing attacks through classification.
We examine the benefits and drawbacks of these machine learning models, highlighting their capacity to identify hitherto unidentified threats, automate judgment calls, and lessen the need for human intervention. The study also explores the real-world difficulties of implementing machine learning models in dynamic network settings, such as problems with data labeling, false-positive rates, model generalization, and security system scalability. The study also discusses how hybrid models, which combine supervised and unsupervised learning techniques, might provide a more comprehensive and flexible security framework.
In terms of accuracy, adaptability, and response times, these ML-based techniques surpass conventional security systems in detecting a variety of network anomalies, according to experimental data from many testbed environments. Performance measures including recall, accuracy, precision, and F1-score are contrasted with more traditional techniques like heuristics and signature-based detection.
According to our research, machine learning (ML)-based anomaly detection has great promise for contemporary network security, offering proactive and instantaneous defenses against a constantly evolving array of cybersecurity risks.
This study demonstrates how machine learning (ML) has the potential to revolutionize network security by making it more intelligent, automated, and responsive to both known and emerging cyberthreats. It does this by thoroughly reviewing a variety of machine learning methods. We also go over potential future research avenues and deployment tactics that could improve machine learning's effectiveness in cybersecurity applications.},
        keywords = {Cybersecurity, Supervised Learning, Unsupervised Learning, Anomaly Detection, Intrusion Detection Systems, Network Security, And Machine Learning.},
        month = {April},
        }