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@article{193485,
        author = {Nambiraj. S and Adhith K.R and Maadhula R},
        title = {Intelligent Vulnerability Assessment and Exploitation Detection System: An AI-Enabled Autonomous Cybersecurity Response Framework},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {901-904},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193485},
        abstract = {The exponential growth of interconnected digital infrastructures has significantly increased organizational exposure to cyber threats. Traditional vulnerability assessment and incident response mechanisms rely on periodic scanning, signature-based detection, and manual intervention, which are inadequate against modern attack techniques such as zero-day exploits, advanced persistent threats, fileless malware, and automated exploitation frameworks. This research article proposes an Intelligent Vulnerability Assessment and Exploitation Detection System integrated with an AI-enabled autonomous cybersecurity response framework. The proposed model combines continuous vulnerability monitoring, behavioral analytics, machine learning-driven exploitation detection, and automated containment strategies to reduce detection latency and improve security posture. By leveraging supervised, unsupervised, and deep learning algorithms, the framework dynamically prioritizes vulnerabilities, detects real-time exploitation attempts, and initiates automated response actions based on risk confidence scores. The system aims to transition cybersecurity operations from reactive defense models to predictive and autonomous protection ecosystems.},
        keywords = {Artificial Intelligence, Vulnerability Assessment, Exploitation Detection, Risk Scoring.},
        month = {March},
        }