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The robust deep learning–based schemes for intrusion detection in Internet of Things environments

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Abstract

With the advent of the Internet of Things (IoT), network attacks have become more diverse and intelligent. In order to ensure the security of the network, Intrusion Detection system (IDS) has become very important. However, when met with the adversarial examples, IDS has itself become no longer secure, and the attackers can increase the success rate of attacks by misleading IDS. Therefore, it is necessary to improve the robustness of the IDS. In this paper, we employ Fast Gradient Sign Method (FGSM) to generate adversarial examples to test the robustness of three intrusion detection models based on convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU). We employ three training methods: the first is to train the models with normal examples, the second is to train the models directly with adversarial examples, and the last is to pretrain the models with normal examples, and then employ adversarial examples to train the models. We evaluate the performance of the three models under different training methods, and find that under normal training method, CNN is the most robust model to adversarial examples. After adversarial training, the robustness of GRU and LSTM to adversarial examples has greatly been improved.

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References

  1. Elrawy MF, Awad AI, Hamed HFA (2018) Intrusion detection systems for IoT-based smart environments: a survey. J Cloud Comput 7:1–20

    Article  Google Scholar 

  2. Weber M, Boban M (2016) Security challenges of the Internet of Things. In: 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), pp 638–643

  3. Putchala MK (2017) Deep Learning Approach for Intrusion Detection System (IDS) in the Internet of Things (IoT) Network using Gated Recurrent Neural Networks (GRU)

  4. Gendreau AA, Moorman M (2016) Survey of intrusion detection systems towards an end to end secure Internet of Things. In: 2016 IEEE 4th international conference on Future Internet of Things and Cloud (FiCloud), pp 84–90

  5. Karatas G, Demir O., Sahingoz O.K. (2018) Deep learning in intrusion detection systems. In: 2018 international congress on big data, deep learning and fighting cyber terrorism (IBIGDELFT), pp 113–116

  6. Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2014) Intriguing properties of neural networks. CoRR, arXiv:1312.6199

  7. Ibitoye O, Shafiq MO, Matrawy A (2019) Analyzing adversarial attacks against deep learning for intrusion detection in IoT networks. In: 2019 IEEE global communications conference (GLOBECOM), pp 1–6

  8. Khamis RA, Shafiq MO (2020) Investigating resistance of deep learning-based IDS against adversaries using min-max Optimization. In: ICC 2020 - 2020 IEEE international conference on communications (ICC), pp 1–7

  9. Khamis RA, Matrawy A (2020) Evaluation of adversarial training on different types of neural networks in deep learning-based IDSs. In: 2020 international symposium on networks, computers and communications (ISNCC), pp 1–6

  10. Sharafaldin I, Lashkari AH, Ghorbani AA (2018) Towards generating a new intrusion detection dataset and intrusion traffic characterization. In: 4th international conference on information systems security and privacy (ICISSP), Portugal

  11. Koroniotis N, Moustafa N, Sitnikova E, Turnbull B (2019) Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT Dataset. arXiv:1811.00701

  12. Wang Z (2018) Deep learning-based intrusion detection with adversaries. IEEE Access 6:38367–38384

    Article  Google Scholar 

  13. Warzynski A, Kolaczek G (2018) Intrusion detection systems vulnerability on adversarial examples. In: 2018 innovations in intelligent systems and applications (INISTA), pp 1–4

  14. Khan RU, Zhang X, Kumar R (2018) Analysis of ResNet and GoogleNet models for malware detection. J Comput Virol Hacking Tech 15:29–37

    Article  Google Scholar 

  15. Kottapalle P (2020) A cnn-lstm model for intrusion detection system from high dimensional data. J Inf Comput Sci 10(3):1362–1370

    Google Scholar 

  16. Athalye A, Engstrom L, Ilyas A, Kwok K (2018) Synthesizing robust adversarial examples. arXiv:1707.07397

  17. Chen P, Zhang H, Sharma Y, Yi J, Hsieh C (2017) ZOO: zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In: Proceedings of the 10th ACM workshop on artificial intelligence and security

  18. Brunner T, Diehl F, Truong-Le M, Knoll A (2019) Guessing smart: biased sampling for efficient black-box adversarial attacks. In: 2019 IEEE/CVF international conference on computer vision (ICCV), pp 4957–4965

  19. Szegedy C, Zaremba W, Sutskever I, Bruna J, Erhan D, Goodfellow I, Fergus R (2014) Intriguing properties of neural networks. CoRR, arXiv:1312.6199

  20. Goodfellow IJ, Goodfellow I, Shlens J, Szegedy C (2015) Explaining and harnessing adversarial examples. CoRR, arXiv:1412.6572

  21. Kurakin A, Goodfellow I, Bengio S (2017) Adversarial machine learning at scale. arXiv:1611.01236

  22. Wang Q, Guo W, Zhang K, Ororbia A, Xing X, Liu X, Giles CL (2017) Adversary resistant deep neural networks with an application to malware detection. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data Mining

  23. Das N, Shanbhogue M, Chen S, Hohman F, Chen L, Kounavis M, Chau DH (2017) Keeping the bad guys out: protecting and vaccinating deep learning with JPEG compression. arXiv:1705.02900

  24. Madry A, Makelov A, Schmidt L, Tsipras D, Vladu A (2018) Towards deep learning models resistant to adversarial attacks. arXiv:1706.06083

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Acknowledgements

The authors thank the anonymous reviewers for helpful suggestions.

Funding

This work was supported by Science and Technology on Communication Networks Laboratory under Grant 6142104180413.

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Authors and Affiliations

  1. School of Cyberspace, Hangzhou Dianzi University, Hangzhou, 310018, People’s Republic of China

    Xingbing Fu & Nan Zhou

  2. Science and Technology on Communication Networks Laboratory, Shijiazhuang, People’s Republic of China

    Xingbing Fu & Libin Jiao

  3. School of Software, Dalian University of Technology, Dalian, 116024, People’s Republic of China

    Haifeng Li

  4. School of Communication Engineering, Hangzhou Dianzi University, Hangzhou, 310018, People’s Republic of China

    Jianwu Zhang

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  1. Xingbing Fu
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  2. Nan Zhou
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Correspondence to Xingbing Fu.

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Fu, X., Zhou, N., Jiao, L. et al. The robust deep learning–based schemes for intrusion detection in Internet of Things environments. Ann. Telecommun. 76, 273–285 (2021). https://doi.org/10.1007/s12243-021-00854-y

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