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@article{204061,
        author = {Prof. Bharti Vivek Bandgar and Kaivalya Kulkarni and Prasad Waghmare and Onkar Pomane and Shubham Waghmare},
        title = {A Comprehensive Review of Intrusion Detection System for Port Scanning Attacks},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {2473-2484},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=204061},
        abstract = {In today's world, cyberattacks are hitting computer networks harder than ever, and old-school defences like firewalls or basic rule-checking just aren't cutting it against sneaky, advanced intruders. Hackers often start with stealthy recon moves, like port scanning, that slip right past traditional setups. Our project introduces a smart Machine Learning-powered Intrusion Detection System (IDS) that keeps a real-time eye on network traffic to spot these malicious scans. It grabs live packets using Scapy, pulls out key network features, and sorts good traffic from bad with a Random Forest classifier. To cut down on false alarms, we added flow-based analysis that looks at patterns across multiple packets in a short time window, instead of judging each one alone. The system fires off instant alerts with clear visual cues and keeps thorough logs for digging into incidents later. Overall, this IDS boosts detection rates, slashes false positives, and delivers a lightweight, practical tool ready for real-world use.},
        keywords = {Intrusion Detection System, Network Security, Machine Learning, Random Forest, Port Scan Detection, Real-Time Monitoring.},
        month = {June},
        }