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@article{188448,
        author = {Nikita Honaro and Mayuri Tapkire},
        title = {An AI-Based Intrusion Detection and Vulnerability Scanning Framework for Secure Networks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {7},
        pages = {2476-2484},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=188448},
        abstract = {Due to the rapid rise of cybercrime and the increased complexity of network attacks, traditional Intrusion Detection Systems (IDS) are in most cases incapable of detecting sophisticated threats and zero-day vulnerabilities. To solve these problems, this article presents an AI-based Intrusion Detection and Vulnerability Scanning Framework that strengthens network defense with the help of intelligent automation. The system in question uses a Random Forest classifier trained on the widely-used NSL-KDD dataset to make accurate distinctions between malicious activity and normal traffic and thus, detection accuracy of 96.8% is achieved. Besides threat detection, there is also a real-time vulnerability scanner that identifies open ports, outdated or misconfigured services, and poses of known exploits before attackers target them. The experimental evaluation indicates that the proposed framework enhances precision and decreases the number of false alarms to a great extent if we compare it with traditional IDS models. In general, this paper is about a cybersecurity solution that is ahead of the curve and thus, organizations can detect, predict, and respond to new threats more efficiently in a continuously changing digital environment.},
        keywords = {Intrusion Detection System (IDS), Cybersecurity, Random Forest, Vulnerability Scanning, Network Security, Machine Learning},
        month = {December},
        }