CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

A Comparative Study of Deep Learning Models for Network Intrusion Detection

by Jyoti Khurana, Vachali Aggarwal, Harjinder Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 23
Year of Publication: 2021
Authors: Jyoti Khurana, Vachali Aggarwal, Harjinder Singh
10.5120/ijca2021921135

Jyoti Khurana, Vachali Aggarwal, Harjinder Singh . A Comparative Study of Deep Learning Models for Network Intrusion Detection. International Journal of Computer Applications. 174, 23 ( Mar 2021), 38-46. DOI=10.5120/ijca2021921135

@article{ 10.5120/ijca2021921135,
author = { Jyoti Khurana, Vachali Aggarwal, Harjinder Singh },
title = { A Comparative Study of Deep Learning Models for Network Intrusion Detection },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2021 },
volume = { 174 },
number = { 23 },
month = { Mar },
year = { 2021 },
issn = { 0975-8887 },
pages = { 38-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume174/number23/31818-2021921135/ },
doi = { 10.5120/ijca2021921135 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:22:56.106234+05:30
%A Jyoti Khurana
%A Vachali Aggarwal
%A Harjinder Singh
%T A Comparative Study of Deep Learning Models for Network Intrusion Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 23
%P 38-46
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the advancement of digital technologies, cybersecurity is attracting more attention as cyber-attacks are becoming more frequent and threatening. A marked upturn has been noticed in the volume and creativity of hacks and cyberattacks. Artificial Intelligence (AI) and Deep Learning (DL) can help address these concerns by contributing to threat detection. They can recognize patterns in data, enabling security systems to learn from former experience. This paper concerns the comparative evaluation of the several techniques of deep learning employed for network intrusion detection.

References
  1. W. Wang, Y. Sheng, J. Wang, X. Zeng, X. Ye, Y. Huang, and M. Zhu, “Hast-ids: Learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection,” IEEE Access, vol. 6, pp. 1792–1806, 2017.
  2. Wu, Peilun & Guo, Hui. “LuNet: A Deep Neural Network for Network Intrusion Detection. 617-624. 10.1109/SSCI44817.2019.9003126.” ,The University of New South Wales, 2019
  3. Wu, Peilun & Guo, Hui. “Pelican: A Deep Residual Network for Network Intrusion Detection.”,School of Computer Science and Engineering, University of New South Wales, Sydney, 2020
  4. Mirsky, Yisroel & Doitshman, Tomer & Elovici, Yuval & Shabtai, Asaf.”Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection. 10.14722/ndss.2018.23211.”, Ben-Gurion University of the Negev, 2018
  5. Fan, Haoyi & Zhang, Fengbin & Li, Zuoyong. “Anomalydae: Dual Autoencoder for Anomaly Detection on Attributed Networks. 5685-5689. 10.1109/ICASSP40776.2020.9053387.”School of Computer Science and Technology, 2020
  6. KDD Cup 99, http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html."
  7. UNSW-NB15dataset, http://www.unsw.adfa.edu.au/australian-centre-for-cyber-security/cybersecurity/ADFA-NB15-Datasets
  8. S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” arXiv preprint arXiv:1502.03167, 2015
  9. Evans, D.: The Internet of Things How the Next Evolution of the Internet Is Changing Everything (2011)
  10. I. Rish,”An empirical study of the naive Bayes classifier”, T.J. Watson Research Center
  11. S. Rasoul Safavian and David Landgrebe, A Survey of Decision tree Classifier Methodology
  12. S.V.N. Vishwanathan, M. Narasimha Murty, “SSVM : A Simple SVM Algorithm”,{vishy, mnm}@csa.iisc.ernet.in Dept. of Comp. Sci. and Automation
  13. Shiyi Yang, Peilun Wu and Hui Guo,”DualNet: Locate Then Detect Effective Payload with Deep Attention Network”, School of Computer Science and Engineering, University of New South Wales, Sydney(2020)
  14. Shibo Zhou and Xiaohua Li, “Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection”, {szhou19, xli}@binghamton.edu (2020)
  15. Faizan Ahmad, Aaima Najam and Zeeshan Ahmed, “Image-based Face Detection and Recognition: “State of the Art”
  16. Xin, M., Wang, Y. Research on image classification model based on deep convolution neural network. J Image Video Proc. 2019, 40 (2019). https://doi.org/10.1186/s13640-019-0417-8
  17. Saad ALBAWI, Tareq Abed MOHAMMED, “Understanding of a Convolutional Neural Network”, ICET2017, Antalya, Turkey
  18. Alex Sherstinsky, “Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network”, arXiv:1808.03314v8 [cs.LG] 21 Dec 2020
  19. Klaus Greff, Rupesh K. Srivastava, Jan Koutn´ık, Bas R. Steunebrink, Jurgen Schmidhuber, “LSTM: A Search Space Odyssey”, arXiv:1503.04069v2 [cs.NE] 4 Oct 2017
  20. Hochreiter, Sepp. (1998). “The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems.” 6. 107-116. 10.1142/S0218488598000094.
  21. Chigozie Enyinna Nwankpa, Winifred Ijomah, Anthony Gachagan, and Stephen Marshall,”Activation Functions: Comparison of Trends in Practice and Research for Deep Learning”, arXiv:1811.03378v1 [cs.LG] 8 Nov 2018
  22. Nagi, Jawad & Ducatelle, Frederick & Di Caro, Gianni & Ciresan, Dan & Meier, Ueli & Giusti, Alessandro & Nagi, Farrukh & Schmidhuber, Jürgen & Gambardella, Luca Maria. (2011). “Max-pooling convolutional neural networks for vision-based hand gesture recognition”. 2011 IEEE International Conference on Signal and Image Processing Applications, ICSIPA 2011. 342-347. 10.1109/ICSIPA.2011.6144164.
  23. Rahul Dey and Fathi M. Salem, “Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks”
  24. Xue Ying 2019 J. Phys,”An Overview of Overfitting and its Solutions”, Conf. Ser. 1168 022022
  25. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov,”Dropout: A Simple Way to Prevent Neural Networks from Overfitting”
  26. Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun, “Deep Residual Learning for Image Recognition”, arXiv:1512.03385v1 [cs.CV] 10 Dec 2015
  27. Franc¸ois Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions”, arXiv:1610.02357v3 [cs.CV] 4 Apr 2017
  28. Dor Bank, Noam Koenigstein, Raja Giryes, “Autoencoders”, arXiv:2003.05991v1 [cs.LG] 12 Mar 2020
  29. Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh, Timothee Masquelier, and Anthony Maida, “Deep Learning in Spiking Neural Networks”, arXiv:1804.08150v4 [cs.NE] 20 Jan 2019
  30. Goodfellow, Y. Bengio, and A. Courville, Deep learning. MIT press, 2016.
  31. S. R. Kheradpisheh, M. Ganjtabesh, S. J. Thorpe, and T. Masquelier, “Stdp-based spiking deep convolutional neural networks for object recognition,” Neural Networks, vol. 99, pp. 56–67, 2018.
  32. Peng, Joanne & Lee, Kuk & Ingersoll, Gary. (2002). An Introduction to Logistic Regression Analysis and Reporting. Journal of Educational Research - J EDUC RES. 96. 3-14. 10.1080/00220670209598786.
  33. Guo, Gongde & Wang, Hui & Bell, David & Bi, Yaxin. (2004). KNN Model-Based Approach in Classification.
  34. Chengsheng, Tu & Huacheng, Liu & Bing, Xu. (2017). AdaBoost typical Algorithm and its application research. MATEC Web of Conferences. 139. 00222. 10.1051/matecconf/201713900222.
  35. Leslie Pack Kaelbling, Michael L. Littman, “Reinforcement Learning: A Survey”
  36. Kaize Ding, Jundong Li, Rohit Bhanushali, and Huan Liu, “Deep anomaly detection on attributed networks,” in Proceedings of the 2019 SIAM International Conference on Data Mining. SIAM, 2019, pp. 594–602.
  37. “Nsl-kdd data set for network-based intrusion detection systems.” http://nsl.cs.unb.ca/KDD/NSL-KDD.html, March 2009.
  38. Kolias, Costantinos & Kambourakis, Georgios. (2015). Aegean Wi-Fi Intrusion Dataset (AWID), http://icsdweb.aegean.gr/awid
Index Terms

Computer Science
Information Sciences

Keywords

Cybersecurity Deep Learning Deep Neural Networks Intrusion Detection Anomaly Detection Network Intrusion Detection

Powered by PhDFocusTM