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@article{187057,
        author = {Ananthi B and Yasotha S and Soniyalakshmi  K},
        title = {INTELLIGENT ANOMALY BASED INTRUSION DETECTION FOR ZERO DAY THREAT MITIGATION},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {3396-3400},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=187057},
        abstract = {Networks face increasing security risks due to attacks that exploit unknown vulnerabilities, including zero-day threats. Existing intrusion detection methods typically analyze pre-stored datasets, which limits their ability to detect new or unforeseen attacks in real time. This paper presents an Intelligent Anomaly-Based Intrusion Detection System capable of continuously monitoring live network traffic and identifying unusual behavior as it occurs. The system uses machine learning techniquesto model typical network activity and detects deviations that may indicate previously unknown attacks. Real-time observation enables faster threat detection, timely response, and stronger protection for critical network resources. Experimental results demonstrate that the proposed system offers higher adaptability, improved accuracy, and practical deployment advantages compared to conventional dataset-dependent approaches. By integrating predictive analytics with continuous traffic monitoring, this approach provides a robust and practical framework for identifying emerging threats and enhancing the overall resilience and security of complex network environments.},
        keywords = {Intrusion Detection, Anomaly Detection, Zero-Day Threats, Real-Time Monitoring, Machine Learning, Network Security.},
        month = {November},
        }