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@article{205280,
        author = {Mr. A. Siva and Mr. S. Derikson},
        title = {An Integrated Cyber Threat Intelligence and Intrusion Detection System Using Machine Learning with Real-Time Monitoring and Response},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {1},
        pages = {5957-5963},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=205280},
        abstract = {The growing sophistication of cyberattacks against modern network infrastructures has exposed critical limitations in traditional signature-based and rule-based intrusion detection systems (IDS). This paper presents an integrated Cyber Threat Intelligence and Intrusion Detection System (CTI-IDS) that combines real-time network packet capture, machine learning-based traffic classification, and a comprehensive suite of cybersecurity response and reporting modules. The proposed system leverages NSL-KDD-style network traffic features and evaluates four machine learning algorithms — Random Forest, XGBoost, Voting Classifier, and Isolation Forest — for classifying network traffic into five categories: Normal, Denial-of-Service (DoS), Probe, Remote-to-Local (R2L), and User-to-Root (U2R). Isolation Forest achieves the highest accuracy of 79.86% and an F1-score of 79.92%. Beyond classification, the system integrates automated alert generation, IP blocking, YARA-based file scanning, IP reputation checking, an AI-powered cybersecurity chatbot, and automated PDF report generation — all accessible through a Flask-based real-time web dashboard. Results confirm the viability of machine learning in real-time intrusion detection and demonstrate the practical benefits of integrating threat intelligence with automated network defense.},
        keywords = {Intrusion Detection System; Cyber Threat Intelligence; Machine Learning; Network Security; Random Forest; XGBoost; Isolation Forest; Real-Time Monitoring; Flask Dashboard; YARA File Scanning.},
        month = {June},
        }