[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
16. Gwon, H., C. Lee, R. Keum, H. Choi. Network Intrusion Detection Based on LSTM and Feature Embedding. – arXiv. Preprint arXiv:1911.11552, 2019.
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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.
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar
[
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
]Search in Google Scholar