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IJIEE
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Published: 2026-09-30

A Lightweight Hybrid Deep Learning Framework for Multi-Class Network Intrusion Detection

Professor, Dept of ECE, Mother Theresa Institute of Engineering and Technology (Autonomous), Palamaner, AP, India
Lead Software Engineer, M&T bank Buffalo, New York
Associate Professor, Department of CSE, Presidency University, Bengaluru, India
Manager, Sogeti USA, Sogeti USA, 8075 Washington Village Drive, Washington Township, Dayton, Ohio 45458
Assistant Professor, Department of AI&DS, Annamacharya Institute of Technology & Sciences, Tirupati, India
Intrusion Detection System CICIDS2017 1D-CNN BiLSTM Temporal Attention Multi-class Classification Network Security

Abstract

The growing variety of cyberattacks has increased the need for intrusion detection systems that can detect multiple categories of attack with high reliability. We propose a lightweight hybrid framework LHCBA-Net, which integrates one dimensional convolutional neural networks, bidirectional long short term memory and temporal attention for multiclass network intrusion detection in this paper. The model was trained after the CICIDS2017 dataset went through a series of data cleaning, feature engineering, feature selection, class balancing and feature transformation procedures. CNN layers capture local traffic patterns, BiLSTM models bidirectional contextual dependencies, and temporal attention highlights informative intrusion related representations. Experimental results on the test set compared with traditional machine learning and deep learning baselines show that LHCBA-Net achieved 99.08% test accuracy with precision, recall, F1-score and AUC of 0.991, 0.990, 0.990 and 0.998 respectively. Feature-importance and SHAP analyzes provide further support for the interpretation of the learned decision patterns. The results show that the proposed framework is efficient for accurate detection of multi-class intrusions.

References

  1. T. Ahmad and D. Truscan, “Early Detection with Explainability of Network Attacks Using Deep Learning,” in 2024 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), Toronto, ON, Canada, 2024.
  2. Uysal and U. Kose, “Analysis of Network Intrusion Detection via Explainable Artificial Intelligence: Applications with SHAP and LIME,” in 2024 Cyber Awareness and Research Symposium (CARS), Grand Forks, ND, USA, 2024.
  3. D. Attique, H. Wang, P. Wang, D. Javeed and M. Adil, “EX-DFL: An Explainable Deep Federated-based Intrusion Detection System for Industrial IoT,” in 2024 21st International Joint Conference on Computer Science and Software Engineering (JCSSE), Phuket, Thailand, 2024.
  4. Mayan, S. Krishanveni and B. Jothi, “AI Enabled Digital Twin Models to Enhance Security in Smart Cities,” in 2024 International Conference on Intelligent Computing and Sustainable Innovations in Technology (IC-SIT), Bhubaneswar, India, 2024.
  5. Z. Xu and Y. Liu, “Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017,” in 2025 10th International Conference on Electronic Technology and Information Science (ICETIS), Hangzhou, China, 2025.
  6. S. Wang, Y. Shi, Z. Duan, G. Zhou and P. Yu, “Research on Lightweight Intrusion Detection Model Based on Improved MobileNetV3,” in 2026 IEEE 8th International Conference on Communications, Information System and Computer Engineering (CISCE), Guangzhou, China, 2026.
  7. J. Mohan and R. K. M, “Intelligent Intrusion Detection System Combining Behavioral Analysis and Machine Learning Methods,” in 2025 3rd International Conference on Data Science and Network Security (ICDSNS), Tiptur, India, 2025.
  8. R. Sindhula and S. Pradeep, “EFSMDCD: Method for Detecting and Categorizing DoS and DDoS Attacks using Ensemble Feature Selection,” in 2025 International Conference on Innovation in Artificial Intelligence and Internet of Things (AIIT), Jeddah, Saudi Arabia, 2025.
  9. P. Abdelhalim, A. Abo-alian and N. Badr, “A Hybrid CNN-LSTM-RFE Approach for Network Intrusion System,” in 2025 International Conference on Artificial Intelligence Science and Applications in Industry and Society (CAISAIS), Galala, Egypt, 2025.
  10. S. Gautam, V. Sharma, D. J. Shah, J. Nariya and P. Panchal, “A Hybrid Approach for Enhanced Security in Network Environments,” in 2025 International Conference on Artificial Intelligence and Machine Vision (AIMV), Gandhinagar, India, 2025.
  11. J. S., R. B., B. D. and R. C., “Balanced-IDS: A Deep Hybrid Model to Overcome Class Imbalance in Network Intrusion Detection,” in 2025 6th International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS), Bengaluru, India, 2025.
  12. M. R. Ilahi, A. G. Putrada and H. R. P. Sailellah, “R-CICIDS2017: Making the IDS Benchmark Realistic for Quantized Models in IoT Deployments,” in 2025 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), Bandung, Indonesia, 2025.
  13. K. Dudi, M. Singh, D. Kumar, N. Kumar and A. Sharmila, “Explainable and Privacy-Preserving Multi-Class Network Intrusion Detection Using SHAP and LightGBM,” in 2025 5th International Conference on Advancement in Electronics & Communication Engineering (AECE), Ghaziabad, India, 2025.
  14. G. Ramasamy, T. H. Vardhan and J. Jyoshitha, “AI-Driven Hybrid Intrusion Detection and Prevention with Edge-Cloud Integration and SOC Dashboard,” in 2026 8th International Conference on Intelligent Sustainable Systems (ICISS), Tirunelveli, India, 2026.

How to Cite

Dr. S Murali Mohan, Gowtham Reddy Kunduru, Dr. Shaik Jaffar Hussain, Narendra Mangala, & Posina Anusha. (2026). A Lightweight Hybrid Deep Learning Framework for Multi-Class Network Intrusion Detection. International Journal of Interpreting Enigma Engineers (IJIEE), 3(3), 11–20. Retrieved from https://ejournal.svgacademy.org/index.php/ijiee/article/view/476

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