• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2022, Volume: 15, Issue: 46, Pages: 2548-2554

Original Article

Semi-Supervised Intrusion Detection Based on Stacking and Feature-Engineering to Handle Data Imbalance

Received Date:17 September 2022, Accepted Date:04 November 2022, Published Date:14 December 2022


Objectives: To design an architecture that can effectively handle the imbalance levels and complexities in the network data to provide qualitative predictions. Methods: Experiments were performed with KDD CUP 99 dataset, NSL- KDD dataset and UNSW- NB15 dataset. Comparisons were performed with SAVAERDNN model. Oversampling technique is used for data balancing, and the stacking architecture handles the issue of overtraining introduced due to oversampling. Findings: The proposed Stacking and Feature engineeringbased Semi-supervised (SFS) model presents a combined architecture that integrates data balancing, feature engineering and a stacking-based prediction model that balances data to reduce imbalance, reduces the data size, and also provides highly effective predictions. Results: indicate 2% increase in accuracy levels on the UNSW-NB15 dataset and 10% increase in accuracy levels in the NSL-KDD dataset. Novelty: The architecture has been designed in a domainspecific manner. Multiple intrusion detection datasets, each with different levels of imbalance, have been used to depict the generic nature of the SFS model.

Keywords: Intrusion Detection; Data Imbalance; Stacking; Feature Engineering; Oversampling; Semi Supervised Learning


  1. YT, Murtugudde G. An efficient algorithm for anomaly intrusion detection in a network. Global Transitions Proceedings. 2021;2(2):255–260. Available from: https://doi.org/10.1016/j.gltp.2021.08.066
  2. Fu Y, Du Y, Cao Z, Li Q, Xiang W. A Deep Learning Model for Network Intrusion Detection with Imbalanced Data. Electronics. 2022;11(6):898. Available from: https://doi.org/10.3390/electronics11060898
  3. Pimsarn C, Boongoen T, Iam-On N, Naik N, Yang L. Strengthening intrusion detection system for adversarial attacks: improved handling of imbalance classification problem. Complex & Intelligent Systems. 2022;8:4863–4880. Available from: https://doi.org/10.1007/s40747-022-00739-0
  4. Guarascio M, Cassavia N, Pisani FS, Manco G. Boosting Cyber-Threat Intelligence via Collaborative Intrusion Detection. Future Generation Computer Systems. 2022;135:30–43. Available from: https://doi.org/10.1016/j.future.2022.04.028
  5. Jordan B, Piazza R, Darley T. 2020. Available from: https://docs.oasis-open.org/cti/stix/v2.1/stix-v2.1.html
  6. Darley T, Kirillov I, Piazza R, Beck D. Taxii™version 2.1 committee specification 01. 2020.
  7. Prasad R, Shankar S. secure intrusion detection system routing protocol for mobile ad-hoc network. Global Transitions Proceedings. 2022;4(4):1–11. Available from: https://doi.org/10.1016/j.gltp.2021.10.003
  8. Wu T, Fan H, Zhu H, You C, Zhou H, Huang X. Intrusion detection system combined enhanced random forest with SMOTE algorithm. EURASIP Journal on Advances in Signal Processing. 2022;2022(1). Available from: https://doi.org/10.1186/s13634-022-00871-6
  9. Siddiqui F, Beley J, Zeadally S, Braught G. Secure and lightweight communication in heterogeneous IoT environments. Internet of Things. 2021;14:100093. Available from: https://doi.org/10.1016/j.iot.2019.100093
  10. Mehmood A, Khanan A, Umar MM, Abdullah S, Ariffin KAZ, Song H. Secure Knowledge and Cluster-Based Intrusion Detection Mechanism for Smart Wireless Sensor Networks. IEEE Access. 2018;6:5688–5694. Available from: https://doi.org/10.1109/ACCESS.2017.2770020
  11. Yang Y, Zheng K, Wu B, Yang Y, Wang X. Network Intrusion Detection Based on Supervised Adversarial Variational Auto-Encoder With Regularization. IEEE Access. 2020;8:42169–42184. Available from: https://doi.org/10.1109/ACCESS.2020.2977007
  12. Asif M, Abbas S, Khan MA, Fatima A, Khan MA, Lee SW. MapReduce based intelligent model for intrusion detection using machine learning technique. Journal of King Saud University - Computer and Information Sciences. 2021. Available from: https://doi.org/10.1016/j.jksuci.2021.12.008
  13. Yao R, Wang N, Liu Z, Chen P, Ma D, Sheng X. Intrusion detection system in the Smart Distribution Network: A feature engineering based AE-LightGBM approach. Energy Reports. 2021;7:353–361. Available from: https://doi.org/10.1016/j.egyr.2021.10.024
  14. Priya AS, Ramesh SB, Kumar. Intrusion Detection using Attribute Subset Selector Bagging (ASUB) to Handle Imbalance and Noise”. International Journal of Computer Science and Network Security. 2022;22:97–102. Available from: https://doi.org/10.22937/IJCSNS.2022.22.5.15
  15. Parashar, Saggu, Garg. Machine learning based framework for network intrusion detection system using stacking ensemble technique. Indian Journal of Engineering and Materials Sciences. 2022;29(04). Available from: https://doi.org/10.56042/ijems.v29i4.46838


© 2022 Priya & Kumar. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


Subscribe now for latest articles and news.