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@article{196638,
        author = {CH. Venkata Sai Rushyendra Kumar and B. Rama Sai Lokesh and A. Pavan and Dr. N. Sundararajulu},
        title = {Ai Based Network Scanning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3216-3221},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196638},
        abstract = {The rapid growth of network infrastructures and the increasing sophistication of cyber threats, traditional network scanning techniques have become insufficient due to high false-positive rates, lack of contextual awareness, and limited adaptability. This paper proposes an Artificial Intelligence (AI)-based network scanning system that enhances conventional scanning methods by incorporating machine learning for intelligent analysis and risk prioritization. The proposed system performs automated network asset discovery and selectively conducts active scans based on learned network behavior patterns. Machine learning models are employed to analyze network traffic, detect anomalies, and identify potentially vulnerable hosts by distinguishing normal and abnormal activities. Additionally, the system integrates vulnerability information with AI-driven risk scoring to prioritize findings based on exploitability and asset criticality. Experimental evaluation demonstrates that the AI-based approach reduces scanning overhead, minimizes false positives, and improves the accuracy of threat identification compared to traditional network scanners. The proposed framework provides a scalable, adaptive, and efficient solution for proactive network security assessment, assisting security teams in early threat detection and informed decision-making.},
        keywords = {— network traffic analysis, AI-based threat detection, anomaly detection, Random Forest, explainable AI, vulnerability risk scoring, network scanning, cyber security, clinical decision support.},
        month = {April},
        }