Abstract
Deep Learning (DL) algorithms are being applied to network intrusion detection, as they can outperform other methods in terms of computational efficiency and accuracy. However, these algorithms have recently been found to be vulnerable to adversarial examples – inputs that are crafted with the intent of causing a Deep Neural Network (DNN) to misclassify with high confidence. Although a significant amount of work has been done to find robust defence techniques against adversarial examples, they still pose a potential risk. The majority of the proposed attack and defence strategies are tailored to the computer vision domain, in which adversarial examples were first found. In this paper, we consider this issue in the Network Intrusion Detection System (NIDS) domain and extend existing adversarial example crafting algorithms to account for the domain-specific constraints in the feature space. We propose to incorporate information about the difficulty of feature manipulation directly in the optimization function. Additionally, we define a novel measure for attack cost and include it in the assessment of the robustness of DL algorithms. We validate our approach on two benchmark datasets and demonstrate successful attacks against state-of-the-art DL network intrusion detection algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Azami, M.E., Lartizien, C., Canu, S.: Converting SVDD scores into probability estimates: application to outlier detection. Neurocomputing 268, 64–75 (2017)
Carlini, N., et al.: On evaluating adversarial robustness. arXiv:1902.06705 [cs, stat] (2019)
Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. arXiv:1608.04644 [cs] (2016)
Carlini, N., Wagner, D.: Adversarial examples are not easily detected: bypassing ten detection methods. arXiv:1705.07263 [cs] (2017)
Dong, B., Wang, X.: Comparison deep learning method to traditional methods using for network intrusion detection. In: 2016 8th IEEE International Conference on Communication Software and Networks (ICCSN), pp. 581–585. IEEE (2016)
Gao, N., Gao, L., Gao, Q., Wang, H.: An intrusion detection model based on deep belief networks. In: 2014 Second International Conference on Advanced Cloud and Big Data, pp. 247–252 (2014)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv:1412.6572 [cs, stat] (2014)
Hartl, A., Bachl, M., Fabini, J., Zseby, T.: Explainability and adversarial robustness for RNNs. arXiv:1912.09855 [cs, stat] (2020)
Hashemi, M.J., Cusack, G., Keller, E.: Towards evaluation of NIDSs in adversarial setting. In: Proceedings of the 3rd ACM CoNEXT Workshop on Big DAta, Machine Learning and Artificial Intelligence for Data Communication Networks - Big-DAMA 2019, pp. 14–21 (2019)
Hawkins, S., He, H., Williams, G., Baxter, R.: Outlier detection using replicator neural networks. In: Kambayashi, Y., Winiwarter, W., Arikawa, M. (eds.) DaWaK 2002. LNCS, vol. 2454, pp. 170–180. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-46145-0_17
Kim, J., Shin, N., Jo, S.Y., Kim, S.H.: Method of intrusion detection using deep neural network. In: 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pp. 313–316 (2017)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv:1412.6980 [cs] (2017)
Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial machine learning at scale. arXiv:1611.01236 [cs, stat] (2017)
Mishra, P., Varadharajan, V., Tupakula, U., Pilli, E.S.: A detailed investigation and analysis of using machine learning techniques for intrusion detection. IEEE Commun. Surv. Tutorials 21, 686–728 (2019)
Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2574–2582 (2016)
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: arXiv:1511.07528 [cs, stat], pp. 372–387, March 2016
Papernot, N., McDaniel, P., Goodfellow, I.: Transferability in machine learning: from phenomena to black-box attacks using adversarial samples. arXiv:1605.07277 [cs] (2016)
Rezvy, S., Petridis, M., Lasebae, A., Zebin, T.: Intrusion detection and classification with autoencoded deep neural network. In: Lanet, J.-L., Toma, C. (eds.) SECITC 2018. LNCS, vol. 11359, pp. 142–156. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12942-2_12
Sharafaldin, I., Habibi Lashkari, A., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: Proceedings of the 4th International Conference on Information Systems Security and Privacy, pp. 108–116 (2018)
Shone, N., Ngoc, T.N., Phai, V.D., Shi, Q.: A deep learning approach to network intrusion detection. IEEE Trans. Emerg. Top. Comput. Intell. 2, 41–50 (2018)
Szegedy, C., et al.: Intriguing properties of neural networks. arXiv:1312.6199 [cs] (2013)
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD CUP 99 data set. In: Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1–6 (2009)
Weng, T.W., et al.: Evaluating the robustness of neural networks: an extreme value theory approach. arXiv:1801.10578 [cs, stat] (2018)
Yang, K., Liu, J., Zhang, C., Fang, Y.: Adversarial examples against the deep learning based network intrusion detection systems. In: MILCOM 2018–2018 IEEE Military Communications Conference (MILCOM), pp. 559–564 (2018)
Zhang, X., Zhou, Y., Pei, S., Zhuge, J., Chen, J.: Adversarial examples detection for XSS attacks based on generative adversarial networks. IEEE Access 8, 10989–10996 (2020)
Acknowledgments
The research leading to this publication was supported by the EU H2020 project SOCCRATES (833481) and the Austrian FFG project Malori (873511).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 IFIP International Federation for Information Processing
About this paper
Cite this paper
Teuffenbach, M., Piatkowska, E., Smith, P. (2020). Subverting Network Intrusion Detection: Crafting Adversarial Examples Accounting for Domain-Specific Constraints. In: Holzinger, A., Kieseberg, P., Tjoa, A., Weippl, E. (eds) Machine Learning and Knowledge Extraction. CD-MAKE 2020. Lecture Notes in Computer Science(), vol 12279. Springer, Cham. https://doi.org/10.1007/978-3-030-57321-8_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-57321-8_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-57320-1
Online ISBN: 978-3-030-57321-8
eBook Packages: Computer ScienceComputer Science (R0)