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@article{203138,
        author = {Komal Sharma},
        title = {Nmap and AI: Integrating Artificial Intelligence into Network Scanning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {11619-11622},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=203138},
        abstract = {This paper reviews the integration of artificial intelligence into Nmap-based network scanning, addressing the limitations of traditional port scanners and identifying opportunities for intelligent automation. We summarize literature on Nmap’s functionality (fast host discovery, port and OS detection) and its scalability issues in Internet-wide scans. We also review AI and machine learning applications in cybersecurity (e.g. ML-based intrusion detection, anomaly analysis) and recent AI-driven vulnerability scanners (such as DQN-based penetration testing and ML-enhanced scanners). The problem is that conventional scanners require manual tuning and miss contextual insights, motivating the objective of creating an AI-augmented Nmap framework. Our contribution is a proposed system combining Nmap with ML algorithms for adaptive scanning, anomaly detection, and automated threat response. Key findings from the literature include Nmap’s strong baseline performance but inability to prioritize risks, the effectiveness of ML/AI for pattern recognition and predictive threat detection, and the gap in real-time intelligent decision-making in scanning tools. The novel approach promises reduced scan overhead, better prioritization of vulnerabilities, and faster detection of novel threats. Potential limitations (data and training requirements, computational cost, and adversarial robustness) are discussed. This abstract summarizes the review findings, states the scope and objectives of the proposed research, and highlights the expected benefits and challenges of integrating AI into network scanning.},
        keywords = {Nmap, AI-driven vulnerability scanning, Machine learning, Intrusion detection systems, Anomaly Detection, Network scanning, Cybersecurity, Vulnerability Assessment, AI-based network scanning, Adaptive scanning, Automated threat detection, AI-enhanced security.},
        month = {May},
        }