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@article{177457,
        author = {Satyam Kumar and Moksha Bhandari and Nandini Rathod and Rayala Viswanath and Tirth Lalani and Dr. Chayapathi AR},
        title = {Real-Time Intrusion Detection System with Deep Learning and Visual Alert Dashboard},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {2026-2034},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177457},
        abstract = {As cyber threats grow more complex and frequent, the need for intelligent and responsive Intrusion Detection Systems (IDS) becomes increasingly critical. This paper introduces RealGuard, a real-time IDS that combines deep learning with a dynamic visual alert dashboard to enhance both detection accuracy and operational usability. Unlike traditional rule-based systems, RealGuard employs a hybrid architecture of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to effectively capture spatial and temporal patterns in network traffic.
Trained on the RT-IoT 2022 dataset, the system demonstrates a detection accuracy exceeding 98% with a false positive rate below 2%, validating its robustness in realistic IoT environments. The integrated dashboard provides real-time visualizations of detected threats, including source IPs, attack types, and severity levels, enabling faster and more informed response by network administrators.
By bridging deep learning with practical deployment features, RealGuard offers a scalable and interpretable IDS solution. The framework not only improves detection capabilities but also supports actionable threat response, making it well-suited for modern cybersecurity operations.},
        keywords = {Intrusion Detection System (IDS), Real-Time Monitoring, Deep Learning, Network Security, Anomaly Detection, Visual Dashboard, Cybersecurity, Threat Detection.},
        month = {May},
        }