Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

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Volume 7 Issue 4
July-August 2026

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Enhanced Hybrid Seq2seq-convlstm Network Intrusion Detection System with Real-time Alerts

Author(s) Ms. Ragireddy Sreya Reddy, Dr. P. PrashanthKumar, Dr. V. Anantha Krishna, Dr. U. Srilakshmi
Country India
Abstract The growing number of cyber-attacks has been emphasising the importance of intelligent and efficient Network Intrusion Detection Systems (NIDS). Traditional intrusion detection methods are ineffective against unknown attacks and have difficulty with processing large-scale network traffic. For accurate and reliable intrusion detection, this paper proposes an Enhanced Hybrid Seq2Seq–ConvLSTM Network Intrusion Detection System. The proposed model is composed of the power of sequence learning of Seq2Seq and the temporal feature extraction of ConvLSTM, enhancing the attack classification performance. To make the system more useful in practice the following features are embedded in the framework: Real-Time Alert Notification, Network Traffic Visualization, LIME based Explainability, dynamic multi-dataset support and an interactive Model Metrics Dashboard. The proposed system is tested with three data sets, namely CIC-IDS2017, UNSW-NB15 and CIC-ToN-IoT. The experimental results show that the detection performance is high, showing the improvement of the Accuracy, Precision, Recall, and F1-Score, and reducing the false positive values and false negative values. The proposed framework will be efficient, explainable, and scalable for real-time network intrusion detection, suitable for a modern cybersecurity application.
Keywords Network Intrusion Detection System (NIDS), Seq2Seq, ConvLSTM, Deep Learning, Cybersecurity, LIME Explainability, Real-Time Alert Notification, Network Traffic Visualization.
Field Computer Applications
Published In Volume 7, Issue 4, July-August 2026
Published On 2026-07-11
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E-ISSN 3048-7641
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