A Survey : Deep Learning based Intrusion Detection Systems for IoT Frameworks

Authors

  • Dr. Nanda Ashwin  Professor, Department of Information Science and Engineering, Bangalore, India

Keywords:

DDoS Attack, Deep Learning, Recurrent Neural Network, LSTM

Abstract

Security problems have become a key issue with the rapid rise of the Internet of Things (IoT) and its integration into numerous areas. Intrusion Detection Systems (IDSs) are critical in protecting IoT networks from potential cyber threats. Since they can automatically extract relevant properties from highly dimensional data, deep learning algorithms have drawn a lot of interest in the field of intrusion detection. This survey article gives an in-depth look at the most recent intrusion detection solutions for IoT frameworks that use deep learning approaches. We examine several approaches, methodologies, and techniques used in the design and implementation of deep learning-based intrusion detection systems. Furthermore, we examine the field's problems, open research concerns, and prospective future paths.

References

  1. Jing, Xuyang, Zheng Yan, and Witold Pedrycz, Security Data Collection and Data Analytics in the Internet: A Survey. IEEE Communications Surveys Tutorials (2018)
  2. Park, Younghee, Nikhil Vijayakumar Kengalahalli, and Sang -Yoon Chang, Distributed Security Network Functions against Botnet Attacks in Software-defined Networks
  3. Famous DDoS attacks, https://www.cloudflare.com/learning/ddos/famous-ddos-attacks
  4. Han, Biao, Xiangrui Yang, Zhigang Sun, Jinfeng Huang, and Jinshu Su. OverWatch: A Cross-Plane DDoS Attack Defense Framework with Col-laborative Intelligence in SDN. Security and Communication Networks 2018
  5. Gkountis, Christos, et al., Lightweight algorithm for protecting SDN controller against DDoS attacks. Wireless and Mobile Networking Conference (WMNC), 2017 10th IFIP. IEEE, 2017.
  6. Sultana, Nasrin, et al., Survey on SDN based network intrusion detection system using machine learning approaches. Peer-to-Peer Networking and Applications (2018): 1-9.
  7. Buczak, Anna L., and Erhan Guven. , A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys Tutorials 18.2 (2016): 1153-1176.
  8. Fadlullah, Zubair, et al. , State-of-the-art deep learning: Evolving machine intelligence toward tomorrows intelligent network traffic control systems. IEEE Communications Surveys Tutorials 19.4 (2017): 2432-2455.
  9. Hatcher, William Grant, and Wei Yu. , A Survey of Deep Learning: Platforms, Applications and Emerging Research Trends. IEEE Access 6 (2018): 24411-24432.
  10. Xin, Yang, et al. , Machine Learning and Deep Learning Methods for Cybersecurity. 2018 IEEE Access.
  11. Zaalouk, Adel, et al. , Orchsec: An orchestrator-based architecture for enhancing network-security using network monitoring and sdn control functions. 2014 IEEE Network Operations and Management Symposium (NOMS).
  12. Mousavi, Seyed Mohammad, and Marc St-Hilaire. , Early detection of DDoS attacks against SDN controllers. International Conference on Computing, Networking, and Communications (ICNC), 2015 IEEE
  13. Wang, Rui, Zhiping Jia, and Lei Ju. , An entropy-based distributed DDoS detection mechanism in software-defined networking. 2015 Trust-com/BigDataSE/ISPA IEEE, 2015, Volume 1
  14. Kalkan, Kbra, et al., JESS: Joint Entropy-Based DDoS Defense Scheme in SDN.IEEE Journal on Selected Areas in Communications 36.10 (2018): 2358-2372.
  15. Mao, Jiewen, Weijun Deng, and Fuke Shen. , DDoS Flooding Attack Detection Based on Joint-Entropy with Multiple Traffic Features. 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). IEEE, 2018.
  16. Sahoo, Kshira Sagar, Mayank Tiwary, and Bibhudatta Sahoo. , Detection of high rate DDoS attack from flash events using information metrics in software defined networks. Communication Systems Networks (COMSNETS), 2018 10th International Conference on. IEEE,2018.
  17. Ye, Jin, et al. , A DDoS Attack Detection Method Based on SVM in Software Defined Network. Security and Communication Networks 2018 (2018).
  18. Phan, Trung V., et al. , OpenFlowSIA: An optimized protection scheme for software-defined networks from flooding attacks. Communications and Electronics (ICCE), 2016 IEEE Sixth International Conference on. IEEE, 2016.
  19. Kokila, R. T., S. Thamarai Selvi, and Kannan Govindarajan. , DDoS detection and analysis in SDN-based environment using support vector machine classifier. Advanced Computing (ICoAC), 2014 Sixth International Conference on. IEEE, 2014.
  20. Khemapatapan, Chaiyaporn. , 2-Stage Soft Defending Scheme Against DDoS Attack Over SDN Based on NB AND SVM.
  21. Braga, Rodrigo, Edjard Mota, and Alexandre Passito. , Lightweight DDoS flooding attack detection using NOX/OpenFlow. Local Computer Networks (LCN), 2010 IEEE 35th Conference on. IEEE, 2010.
  22. Nam, Tran Manh, et al. , Self-organizing map-based approaches in DDoS flooding detection using SDN. 2018 International Conference on Information Networking (ICOIN). IEEE, 2018.
  23. Wang, Wentao, Xuan Ke, and Lingxia Wang , A HMM-R Approach to Detect L-DDoS Attack Adaptively on SDN Controller. Future Internet 10.9 (2018): 83
  24. Barki, Lohit, et al. , Detection of distributed denial of service attacks in software defined networks. Advances in Computing, Communications and Informatics (ICACCI), 2016 International. Conference on. IEEE, 2016.
  25. Niyaz, Quamar, Weiqing Sun, and Ahmad Y. Javaid. , A deep learning based DDoS detection system in software-defined networking (SDN). arXiv preprint arXiv:1611.07400 (2016).
  26. Li, Chuanhuang, et al. , Detection and defense of DDoS attackbased on deep learning in OpenFlowbased SDN. International Journal of Communication Systems 31.5 (2018): e3497.
  27. Tang, Tuan A., et al. , Deep learning approach for network intrusion detection in software defined networking.Wireless Networks and Mobile Communications (WINCOM), 2016 International Conference on.IEEE, 2016.
  28. Wang, Wei, et al. , HAST-IDS: learning hierarchical spatial-temporal features using deep neural networks to improve intrusion detection IEEE Access 6 (2018): 1792-1806.
  29. Ishitaki, Taro, et al. , Application of Deep Recurrent Neural Networks for Prediction of User Behavior in Tor Networks. Advanced Infor-mation Networking and Applications Workshops (WAINA), 2017 31st International Conference on. IEEE, 2017.
  30. Javaid, Ahmad, et al. , A deep learning approach for network intrusion detection system. Proceedings of the 9th EAI International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering), 2016.
  31. Tang, Tuan A. , et al., Deep recurrent neural network for intrusion detection in sdn-based networks. 2018 4th IEEE Conference on Network Softwarization and Workshops (NetSoft). IEEE, 2018.
  32. Yuan, Xiaoyong, Chuanhuang Li, and Xiaolin Li. , DeepDefense: Identifying DDoS Attack via Deep Learning. 2017 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, 2017.
  33. Yadav, Satyajit, and Selvakumar Subramanian. , Detection of Application Layer DDoS attack by feature learning using Stacked AutoEncoder. Computational Techniques in Information and Communication Technolo- gies (ICCTICT), 2016 International Conference on. IEEE, 2016.
  34. Imamverdiyev, Yadigar, and Fargana Abdullayeva. , Deep Learning Method for Denial of Service Attack Detection Based on Restricted Boltzmann Machine. Big Data 6.2 (2018): 159-169.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2022-06-30

Issue

Volume 9, Issue 3, May-June-2022

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Dr. Nanda Ashwin "A Survey : Deep Learning based Intrusion Detection Systems for IoT Frameworks " International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011,Volume 9, Issue 3, pp.616-623, May-June-2022.