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@article{198557,
        author = {MOHAMMED JUNED SHAIKH and Mohtaseem Khan and Rehan Khan and Husain Ansari and Saad Shaikh},
        title = {Cyber-Sentinel: A Smart AI-Driven Cyber Guard for Continuous Threat Monitoring and Mitigation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {9206-9214},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=198557},
        abstract = {With the exponential growth of digital systems, cybersecurity threats have become increasingly sophisticated and dynamic. Traditional rule-based security mechanisms fail to detect novel and evolving attacks in real time. This paper proposes Cyber Sentinel, an AI-driven cybersecurity framework that integrates Machine Learning techniques with real-time monitoring systems to detect, analyse, and mitigate cyber threats proactively. The system leverages anomaly detection, behavioural analysis, and predictive modelling to enhance threat detection accuracy. The proposed architecture incorporates Python-based machine learning models with a scalable Node.js backend for continuous monitoring and automated response. Experimental evaluation using benchmark datasets demonstrates improved detection rates and reduced false positives compared to traditional systems The system also incorporates QR code URL extraction and analysis, allowing detection of malicious links embedded within QR codes},
        keywords = {Machine Learning, Intrusion Detection System, AI Security, Threat Detection, Anomaly Detection, Cyber Sentinel},
        month = {April},
        }