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@article{193483,
        author = {R.M.Sanjeevini and O.A.Thapasya and Amarnath M},
        title = {SentinAI: Automated Network Anomaly Detection and Response System using Machine Learning Secure Gate Pro: USB Threat Sterilization and Forensic Analysis Station},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {670-674},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193483},
        abstract = {The rapid evolution of cyber threats has exposed significant vulnerabilities in enterprise networks and portable storage devices. Traditional signature-based security mechanisms often fail to detect zero-day attacks and sophisticated intrusion attempts. This paper presents SentinAI, an Automated Network Anomaly Detection and Response System powered by Machine Learning, and Secure Gate Pro, a USB Threat Sterilization and Forensic Analysis Station. SentinAI leverages behavioral analytics and supervised learning algorithms to detect anomalies in real time and initiate automated containment responses. Secure Gate Pro focuses on physical media security by detecting, neutralizing, and documenting malicious content from USB devices before system interaction. The integration of AI-driven network intelligence with hardware-level threat sterilization creates a comprehensive, multilayered cybersecurity framework suitable for enterprise and institutional environments.},
        keywords = {Cybersecurity, Anomaly Detection, Machine Learning, Network Security.},
        month = {March},
        }