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@article{200113,
        author = {Seetaram Sharma},
        title = {Intrusion Detection System for Detecting System Vulnerabilities Using Machine Learning and Network Security Metrics},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {614-624},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200113},
        abstract = {With the rapid growth of network-based systems, cybersecurity threats have become increasingly sophisticated. Traditional intrusion detection systems (IDS) struggle to detect modern attacks due to their reliance on signature-based techniques. This paper presents a machine learning-based intrusion detection system capable of identifying system vulnerabilities through real-time network monitoring. The proposed system analyzes key parameters such as total packets scanned, threats detected, system integrity, real traffic volume, and recent alerts. Various machine learning algorithms are employed to classify network traffic as normal or malicious. Experimental results demonstrate improved detection accuracy and faster response time compared to conventional methods.},
        keywords = {Intrusion Detection System, Machine Learning, Network Security, System Vulnerability, Real-Time Traffic, Cybersecurity},
        month = {May},
        }