[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

DDoS Cyber-Attacks Detection-Based Hybrid CNN-LSTM

  • Conference paper
  • First Online:
Proceedings of Third International Conference on Computing and Communication Networks (ICCCN 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 917))

Included in the following conference series:

  • 156 Accesses

Abstract

Protecting software-defined networking (SDN) against cyber-attacks has become crucial in an expanding digital threat environment. Distributed Denial-of-Service (DDoS) attacks are risky since they may seriously interrupt operations. To mitigate these risks, this study introduces an anomaly detection method that utilizes a hybrid convolutional and short-term memory (CNN-LSTM) deep neural network. This model merges the CNN's ability to automatically extract spatial features with the LSTM's proficiency in sequence modeling, thereby enhancing the detection of anomalies in network traffic metadata. The model also integrates an autoencoder structure to facilitate representation learning and reduce dimensionality. The model's effectiveness was tested using publicly accessible SDN datasets, and the results were remarkable. The model identified DDoS attacks with an accuracy rate of over 99%, surpassing the performance of previous shallow learning models. Moreover, the model proved highly adaptable, successfully detecting attacks across various data samples. This deep learning-based detection system is a significant advancement, providing precise and efficient analytics that bolster real-time cybersecurity monitoring. However, it's crucial to continue research in deployment, interpretability, and the potential of combinatorial learning with other advanced technologies. We can only fully harness the great potential of artificial intelligence for adequate cyber protection by looking into these areas.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 159.50
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 199.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Simalango, M.F., Kang, M.Y., Oh, S.: Towards constraint-based high performance cloud system in the process of cloud computing adoption in an organization. ArXiv (2010)

    Google Scholar 

  2. Miettinen, M., Sadeghi, A.R.: Keynote: Internet of things or threats? On building trust in IoT. In: 2018 International Conference on Hardware/Software Codesign and System Synthesis, CODES+ISSS 2018 (2018). https://doi.org/10.1109/CODESISSS.2018.8525931

  3. Abomhara, M., Køien, G.M.: Cyber security and the internet of things: vulnerabilities, threats, intruders and attacks (2015). https://doi.org/10.13052/jcsm2245-1439.414

  4. Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., Ayyash, M.: Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17(4), 2347–2376 (2015). https://doi.org/10.1109/COMST.2015.2444095

    Article  Google Scholar 

  5. Ahmed S., et al.: Effective and efficient DDoS attack detection using deep learning algorithm, multi-layer perceptron. Future Internet 2023 15(2), 76 (2023). https://doi.org/10.3390/FI15020076

  6. Singh, K., Singh, P., Kumar, K.: Application layer HTTP-GET flood DDoS attacks: research landscape and challenges. Comput. Secur. 65, 344–372 (2017). https://doi.org/10.1016/J.COSE.2016.10.005

    Article  Google Scholar 

  7. Khuphiran, P., Leelaprute, P., Uthayopas, P., Ichikawa, K., Watanakeesuntorn, W.: Performance comparison of machine learning models for DDoS attacks detection. In: 2018 22nd International Computer Science and Engineering Conference, ICSEC 2018 (2018). https://doi.org/10.1109/ICSEC.2018.8712757

  8. Farnaaz, N., Jabbar, M.: Random forest modeling for network intrusion detection system. Proc. – Proc. Comput. Sci. 89, 213–217 (2016). https://doi.org/10.1016/j.procs.2016.06.047

    Article  Google Scholar 

  9. Li, Y., Guo, L.: An active learning based TCM-KNN algorithm for supervised network intrusion detection. Comput. Secur. 26(7–8), 459–467 (2007). https://doi.org/10.1016/J.COSE.2007.10.002

    Article  Google Scholar 

  10. Panda, M., Patra, M.: Network Intrusion Detection Using Naïve Bayes (2007)

    Google Scholar 

  11. Li, Y., Zhang, B.: An intrusion detection model based on multi-scale CNN. In: Proceedings of 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference, ITNEC 2019, pp. 214–218 (2019). https://doi.org/10.1109/ITNEC.2019.8729261

  12. Aydın, H., Orman, Z., Aydın, M.A.: A long short-term memory (LSTM)-based distributed denial of service (DDoS) detection and defense system design in public cloud network environment. Comput. Secur. 118, 102725 (2022). https://doi.org/10.1016/J.COSE.2022.102725

    Article  Google Scholar 

  13. Javaheri, D., Gorgin, S., Lee, J.A., Masdari, M.: Fuzzy logic-based DDoS attacks and network traffic anomaly detection methods: Classification, overview, and future perspectives. Inf. Sci. (N. Y.) 626, 315–338 (2023). https://doi.org/10.1016/J.INS.2023.01.067

    Article  Google Scholar 

  14. Basnet, M., Ali, M.H.: WCGAN-based cyber-attacks detection system in the EV charging infrastructure. In: 2022 4th International Conference on Smart Power and Internet Energy Systems, SPIES 2022, pp. 1761–1766 (2022). https://doi.org/10.1109/SPIES55999.2022.10082342

  15. Roopak, M., Tian, G.Y., Chambers, J.: An intrusion detection system against DDoS attacks in IoT networks. In: 2020 10th Annual Computing and Communication Workshop and Conference. CCWC 2020, 562–567 (2020). https://doi.org/10.1109/CCWC47524.2020.9031206

    Article  Google Scholar 

  16. Bouyeddou, B., Harrou, F., Kadri, B., Sun, Y.: Detecting network cyber-attacks using an integrated statistical approach. Cluster Comput 24(2), 1435–1453 (2021). https://doi.org/10.1007/S10586-020-03203-1/TABLES/8

    Article  Google Scholar 

  17. Al-Juboori, S.A.M., Hazzaa, F., Jabbar, Z.S., Salih, S., Gheni, H.M.: Man-in-the-middle and denial of service attacks detection using machine learning algorithms. Bull. Electr. Engin. Inf. 12(1), 418–426 (2023). https://doi.org/10.11591/EEI.V12I1.4555

    Article  Google Scholar 

  18. Yildiz, B.: Coding Theory Lecture Notes By Yildiz, pp. 1–63 (2011)

    Google Scholar 

  19. Diaba, S.Y., Elmusrati, M.: Proposed algorithm for smart grid DDoS detection based on deep learning. Neural Netw. 159, 175–184 (2023). https://doi.org/10.1016/J.NEUNET.2022.12.011

    Article  Google Scholar 

  20. Aswad, F.M., Ahmed, A.M.S., Alhammadi, N.A.M., Khalaf, B.A., Mostafa, S.A.: Deep learning in distributed denial-of-service attacks detection method for Internet of Things networks. J. Intell. Syst. 32(1) (2023). https://doi.org/10.1515/JISYS-2022-0155/MACHINEREADABLECITATION/RIS

  21. Chua, L.O., Roska, T.: The CNN Paradigm. IEEE Trans. Circuits Syst. I: Fundamen. Theory Appl. 40(3), 147–156 (1993). https://doi.org/10.1109/81.222795

    Article  Google Scholar 

  22. Yu, Y., Si, X., Hu, C., Zhang, J.: A review of recurrent neural networks: LSTM cells and network architectures. Neural Comput. 31(7), 1235–1270 (2019). https://doi.org/10.1162/NECO_A_01199

    Article  MathSciNet  Google Scholar 

  23. Farahnakian, F., Heikkonen, J.: A deep auto-encoder based approach for intrusion detection system. In: International Conference on Advanced Communication Technology, ICACT 2018, 178–183 (2018). https://doi.org/10.23919/ICACT.2018.8323688

    Article  Google Scholar 

  24. Tayfour, O.E., Marsono, M.N.: Collaborative detection and mitigation of DDoS in software-defined networks. J. Supercomput. 77(11), 13166–13190 (2021). https://doi.org/10.1007/S11227-021-03782-9/TABLES/14

    Article  Google Scholar 

  25. Ivanova, V., Tashev, T., Draganov, I., Ivanova, V., Tashev, T., Draganov, I.: Detection of IoT based DDoS attacks by network traffic analysis using feedforward neural networks. Int. J. Circuits, Syst. Signal Process. 16, 653–662 (2022). https://doi.org/10.46300/9106.2022.16.81

    Article  Google Scholar 

  26. Prasad, A., Chandra, S.: VMFCVD: an optimized framework to combat volumetric DDoS attacks using machine learning. Arab. J. Sci. Eng. 47, 9965–9983 (2022). https://doi.org/10.1007/s13369-021-06484-9

    Article  Google Scholar 

  27. Al-asadi,T.A., Obaid, A.J.: An efficient web usage mining algorithm based on log file data. J. Theoret. Appl. Inf. Technol. 16, 92(2), 215–224 (2016)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Iraqi Commission for Computers and Information/Informatics Institute for Postgraduate Studies, Baghdad, Iraq

    Thura Jabbar Khaleel

  2. Computer Engineering Department, College of Engineering, University of Baghdad, Baghdad, Iraq

    Nadia Adnan Shiltagh

Authors
  1. Thura Jabbar Khaleel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nadia Adnan Shiltagh
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thura Jabbar Khaleel .

Editor information

Editors and Affiliations

  1. University of Calabria, Arcavacata, Rende CS, Italy

    Giancarlo Fortino

  2. Department of Computing and Mathematics, Manchester Metropolitan University, Manchester, UK

    Akshi Kumar

  3. Department of Information Technology, Bhagwan Parshuram Institute of Technolog, New Delhi, Delhi, India

    Abhishek Swaroop

  4. Department of Computing, Faculty of Engineering, Imperial College London, London, UK

    Pancham Shukla

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Khaleel, T.J., Shiltagh, N.A. (2024). DDoS Cyber-Attacks Detection-Based Hybrid CNN-LSTM. In: Fortino, G., Kumar, A., Swaroop, A., Shukla, P. (eds) Proceedings of Third International Conference on Computing and Communication Networks. ICCCN 2023. Lecture Notes in Networks and Systems, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-97-0892-5_41

Download citation

  • DOI: https://doi.org/10.1007/978-981-97-0892-5_41

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-0891-8

  • Online ISBN: 978-981-97-0892-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation