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Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models

Open Access
|Dec 2021

References

  1. 1. Abeshu, A., N. Chilamkurti. Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing. – IEEE Communications Magazine, Vol. 56, 2018, No 2, pp. 169-175.10.1109/MCOM.2018.1700332
  2. 2. Ahmad, H., A. Arif, A. M. Khattak, A. Habib, M. Z. Asghar, B. Shah. Applying Deep Neural Networks for Predicting Dark Triad Personality Trait of Online Users. – In: Proc. of 2020 International Conference on Information Networking (ICOIN’20), IEEE, 2020, pp. 102-105.10.1109/ICOIN48656.2020.9016525
  3. 3. Aldweesh, A., A. Derhab, A. Z. Emam. Deep Learning Approaches for Anomaly-Based Intrusion Detection Systems: A Survey, Taxonomy, and Open Issues. – Knowledge-Based Systems, Vol. 189, 2020, pp. 105-124.10.1016/j.knosys.2019.105124
  4. 4. Alom, M. Z., V. R. Bontupalli, T. M. Taha. Intrusion Detection Using Deep Belief Networks. – In: Proc. of 2015 National Aerospace and Electronics Conference (NAECON’15), IEEE, 2015, pp. 339-344.10.1109/NAECON.2015.7443094
  5. 5. Alrawashdeh, K., C. Purdy. Toward an Online Anomaly Intrusion Detection System Based on Deep Learning. – In: Proc. of 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA’16), IEEE, 2016, pp. 195-200.10.1109/ICMLA.2016.0040
  6. 6. Basumallik, S., R. Ma, S. Eftekharnejad. Packet-Data Anomaly Detection in PMU-Based State Estimator Using Convolutional Neural Network. – International Journal of Electrical Power & Energy Systems, Vol. 107, 2019, pp. 690-702.10.1016/j.ijepes.2018.11.013
  7. 7. Berman, D. S., A. L. Buczak, J. S. Chavis, C. L. Corbett. A Survey of Deep Learning Methods for Cyber Security. – Information, Vol. 10, 2019, No 4. 122.10.3390/info10040122
  8. 8. Chang, R.-I., L.-B. Lai, W.-D. Su, J.-C. Wang, J.-S. Kouh. Intrusion Detection by Backpropagation Neural Networks with Sample-Query and Attribute-Query. – International Journal of Computational Intelligence Research, Vol. 3, 2007, No 1, pp. 6-10.10.5019/j.ijcir.2007.76
  9. 9. Dahiya, P., D. K. Srivastava. Network Intrusion Detection in Big Dataset Using Spark. – Procedia Computer Science, Vol. 132, 2018, pp. 253-262.10.1016/j.procs.2018.05.169
  10. 10. Diro, A., N. Chilamkurti. Leveraging LSTM Networks for Attack Detection in Fog-to-Things Communications. – IEEE Communications Magazine, Vol. 56, 2018, No 9, pp. 124-130.10.1109/MCOM.2018.1701270
  11. 11. Ferrag, M. A., L. Maglaras, S. Moschoyiannis, H. Janicke. Deep Learning for Cyber Security Intrusion Detection: Approaches, Datasets, and Comparative Study. – Journal of Information Security and Applications, Vol. 50, 2020. 102419.10.1016/j.jisa.2019.102419
  12. 12. Gamage, S., J. Samarabandu. Deep Learning Methods in Network Intrusion Detection: A Survey and an Objective Comparison. – Journal of Network and Computer Applications, Vol. 169, 2020. 102767.10.1016/j.jnca.2020.102767
  13. 13. Gao, M., L. Ma, H. Liu, Z. Zhang, Z. Ning, J. Xu. Malicious Network Traffic Detection Based on Deep Neural Networks and Association Analysis. – Sensors, Vol. 20, 2020, No 5. 1452.10.3390/s20051452708576532155834
  14. 14. Gao, X., C. Shan, C. Hu, Z. Niu, Z. Liu. An Adaptive Ensemble Machine Learning Model for Intrusion Detection. – IEEE Access, Vol. 7, 2019, pp. 82512-82521.10.1109/ACCESS.2019.2923640
  15. 15. Guo, K., S. Han, S. Yao, Y. Wang, Y. Xie, H. Yang. Software-Hardware Codesign for Efficient Neural Network Acceleration. – IEEE Micro, Vol. 37, 2017, No 2, pp. 18-25.10.1109/MM.2017.39
  16. 16. Gwon, H., C. Lee, R. Keum, H. Choi. Network Intrusion Detection Based on LSTM and Feature Embedding. – arXiv. Preprint arXiv:1911.11552, 2019.
  17. 17. Jiang, F., Y. Fu, B. B. Gupta, F. Lou, S. Rho, F. Meng, Z. Tian. Deep Learning Based Multi-Channel Intelligent Attack Detection for Data Security. – IEEE Transactions on Sustainable Computing, 2018.
  18. 18. Kang, M.-J., J.-W. Kang. Intrusion Detection System Using Deep Neural Network for In-Vehicle Network Security. – PloS One, Vol. 11, 2016, No 6. e0155781.10.1371/journal.pone.0155781489642827271802
  19. 19. Kim, J., J. Kim, H. L. T. Thu, H. Kim. Long Short Term Memory Recurrent Neural Network Classifier for Intrusion Detection. – In: Proc. of 2016 International Conference on Platform Technology and Service (PlatCon’16), IEEE, 2016, pp. 1-5.10.1109/PlatCon.2016.7456805
  20. 20. Larriva-Novo, X. A., M. Vega-Barbas, V. A. Villagrá, M. S. Rodrigo. Evaluation of Cybersecurity Data Set Characteristics for Their Applicability to Neural Networks Algorithms Detecting Cybersecurity Anomalies. – IEEE Access, Vol. 8, 2020, pp. 9005-9014.10.1109/ACCESS.2019.2963407
  21. 21. Loukas, G., T. Vuong, R. Heartfield, G. Sakellari, Y. Yoon, D. Gan. Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning. – IEEE Access, Vol. 6, 2017, pp. 3491-3508.10.1109/ACCESS.2017.2782159
  22. 22. Ma, T., F. Wang, J. Cheng, Y. Yu, X. Chen. A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks. – Sensors, Vol. 16, 2016, No 10, 1701.10.3390/s16101701508748927754380
  23. 23. Maimó, L. F., Á. L. P. Gómez, F. J. G. Clemente, M. G. Pérez, G. M. Pérez. A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks. – IEEE Access, Vol. 6, 2018, pp. 7700-7712.10.1109/ACCESS.2018.2803446
  24. 24. Montavon, G., W. Samek, K.-R. Müller. Methods for Interpreting and Understanding Deep Neural Networks. – Digital Signal Processing, Vol. 73, 2018, pp. 1-15.10.1016/j.dsp.2017.10.011
  25. 25. Nguyen, G., S. Dlugolinsky, V. Tran, Á. L. García. Deep Learning for Proactive Network Monitoring and Security Protection. – IEEE Access, Vol. 8, 2020, pp. 19696-19716.10.1109/ACCESS.2020.2968718
  26. 26. Pandey, A., S. Thaseen, C. A. Kumar, G. Li. Identification of Botnet Attacks Using Hybrid Machine Learning Models. – In: Proc. of International Conference on Hybrid Intelligent Systems, Cham, Springer, 2019, pp. 249-257.10.1007/978-3-030-49336-3_25
  27. 27. Rajagopal, S., P. P. Kundapur, K. S. Hareesha. A Stacking Ensemble for Network Intrusion Detection Using Heterogeneous Datasets. – Security and Communication Networks, Vol. 2020, 2020.10.1155/2020/4586875
  28. 28. Ring, M., S. Wunderlich, D. Scheuring, D. Landes, A. Hotho. A Survey of Network-Based Intrusion Detection Data Sets. – Computers & Security, Vol. 86, 2019, pp. 147-167.10.1016/j.cose.2019.06.005
  29. 29. Staudemeyer, R. C. Applying Long Short-Term Memory Recurrent Neural Networks to Intrusion Detection. – South African Computer Journal, Vol. 56, 2015, No 1, pp. 136-154.10.18489/sacj.v56i1.248
  30. 30. Thaseen, I. S., B. Poorva, P. S. Ushasree. Network Intrusion Detection Using Machine Learning Techniques. – In: Proc. of 2020 International Conference on Emerging Trends in Information Technology and Engineering (IC-ETITE’20), IEEE, 2020, pp. 1-7.
  31. 31. Vinayakumar, R., M. Alazab, K. P. Soman, P. Poornachandran, A. Al-Nemrat, S. Venkatraman. Deep Learning Approach for Intelligent Intrusion Detection System. – IEEE Access, Vol. 7, 2019, pp. 41525-41550.10.1109/ACCESS.2019.2895334
  32. 32. Wu, P., H. Guo, N. Moustafa. Pelican: A Deep Residual Network for Network Intrusion Detection. – In: Proc. of 2020 50th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W’20), IEEE, 2020, pp. 55-62.
  33. 33. Yu, Y., J. Long, Z. Cai. Network Intrusion Detection through Stacking Dilated Convolutional Autoencoders. – Security and Communication Networks, Vol. 2017, 2017.10.1155/2017/4184196
  34. 34. Zhang, Y., X. Chen, L. Jin, X. Wang, D. Guo. Network Intrusion Detection: Based on Deep Hierarchical Network and Original Flow Data. – IEEE Access, Vol. 7, 2019, pp. 37004-37016.10.1109/ACCESS.2019.2905041
  35. 35. Zhang, Z., X. Zhou, X. Zhang, L. Wang, P. Wang. A Model Based on Convolutional Neural Network for Online Transaction Fraud Detection. – Security and Communication Networks, Vol. 2018, 2018.10.1155/2018/5680264
  36. 36. Zeng, Y., H. Gu, W. Wei, Y. Guo. “Deep-Full-Range”: A Deep Learning Based Network Encrypted Traffic Classification and Intrusion Detection Framework. – IEEE Access, Vol. 7, 2019, pp. 45182-45190.10.1109/ACCESS.2019.2908225
  37. 37. Khan, F. A., A. Gumaei, A. Derhab, A. Hussain. A Novel Two-Stage Deep Learning Model for Efficient Network Intrusion Detection. – IEEE Access, Vol. 7, 2019, pp. 30373-30385.10.1109/ACCESS.2019.2899721
  38. 38. Sumaiya, T. I., C. A. Kumar, A. Ahmad. Integrated Intrusion Detection Model Using Chi-Square Feature Selection and Ensemble of Classifiers. – Arabian Journal for Science and Engineering, Vol. 44, 2019, No 4, pp. 3357-3368.10.1007/s13369-018-3507-5
DOI: https://doi.org/10.2478/cait-2021-0037 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 175 - 188
Submitted on: Jul 29, 2021
Accepted on: Sep 3, 2021
Published on: Dec 7, 2021
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year

© 2021 Sumaiya Thaseen Ikram, Aswani Kumar Cherukuri, Babu Poorva, Pamidi Sai Ushasree, Yishuo Zhang, Xiao Liu, Gang Li, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.