Abstract
During the last decade, machine learning algorithms have massively integrated the defense arsenal made available to security professionals, especially for intrusion detection. However, and despite the progress made in this area, machine learning models have been found to be vulnerable to slightly modified data samples called adversarial examples. Thereby, a small and well-computed perturbation may allow adversaries to evade intrusion detection systems. Numerous works have already successfully applied adversarial examples to network intrusion detection datasets. Yet little attention was given so far to the practicality of these examples in the implementation of end-to-end network attacks. In this paper, we study the applicability of network attacks based on adversarial examples in real networks. We minutely analyze adversarial examples generated with state-of-the-art algorithms to evaluate their consistency based on several criteria. Our results show a large proportion of invalid examples that are unlikely to lead to real attacks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abou Khamis, R., Shafiq, O., Matrawy, A.: Investigating resistance of deep learning-based IDS against adversaries using min-max optimization. arXiv preprint:1910.14107 (2019)
Alhajjar, E., Maxwell, P., Bastian, N.D.: Adversarial machine learning in network intrusion detection systems. arXiv preprint:2004.11898 (2020)
Biggio, B., et al.: Evasion attacks against machine learning at test time. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8190, pp. 387–402. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40994-3_25
Biggio, B., Nelson, B., Laskov, P.: Poisoning attacks against support vector machines. arXiv preprint:1206.6389 (2012)
Biggio, B., Roli, F.: Wild patterns: Ten years after the rise of adversarial machine learning. Pattern Recognition (2018)
Carlini, N., Wagner, D.: Towards evaluating the robustness of neural networks. In: 2017 IEEE Symposium on Security and Privacy. IEEE (2017)
Chen, P.Y., Zhang, H., Sharma, Y., Yi, J., Hsieh, C.J.: Zoo: zeroth order optimization based black-box attacks to deep neural networks without training substitute models. In: 10th ACM Workshop on Artificial Intelligence and Security (2017)
Clements, J., Yang, Y., Sharma, A., Hu, H., Lao, Y.: Rallying adversarial techniques against deep learning for network security. arXiv preprint:1903.11688 (2019)
Fredrikson, M., Jha, S., Ristenpart, T.: Model inversion attacks that exploit confidence information and basic countermeasures. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (2015)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems (2014)
Goodfellow, I.J., Shlens, J., Szegedy, C.: Explaining and harnessing adversarial examples. arXiv preprint:1412.6572 (2014)
Ibitoye, O., Shafiq, O., Matrawy, A.: Analyzing adversarial attacks against deep learning for intrusion detection in IoT networks. In: IEEE Global Communications Conference (GLOBECOM) (2019)
Jagielski, M., Carlini, N., Berthelot, D., Kurakin, A., Papernot, N.: High accuracy and high fidelity extraction of neural networks. In: 29th USENIX Security Symposium (2020)
Klambauer, G., Unterthiner, T., Mayr, A., Hochreiter, S.: Self-normalizing neural networks. In: Advances in Neural Information Processing Systems (2017)
Koroniotis, N., Moustafa, N., Sitnikova, E., Turnbull, B.: Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-IoT dataset. arXiv preprint:1811.00701 (2018)
Kurakin, A., Goodfellow, I., Bengio, S.: Adversarial machine learning at scale. arXiv preprint:1611.01236 (2016)
Lin, Z., Shi, Y., Xue, Z.: IDSGAN: generative adversarial networks for attack generation against intrusion detection. arXiv preprint:1809.02077 (2018)
Martins, N., Cruz, J.M., Cruz, T., Abreu, P.H.: Analyzing the footprint of classifiers in adversarial denial of service contexts. In: EPIA Conference on Artificial Intelligence (2019)
Martins, N., Cruz, J.M., Cruz, T., Abreu, P.H.: Adversarial machine learning applied to intrusion and malware scenarios: a systematic review. IEEE Access 8, 35403–35419 (2020)
Moisejevs, I.: Adversarial attacks and defenses in intrusion detection systems: A survey. Int. J. Artif. Intell. Expert Syst. (IJAE) 8(3), 44–62 (2019)
Moosavi-Dezfooli, S.M., Fawzi, A., Frossard, P.: DeepFool: a simple and accurate method to fool deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
Moustafa, N., Slay, J.: UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In: Military Communications and Information Systems Conference (MilCIS) (2015)
Nicolae, M.I., et al.: Adversarial robustness toolbox v1.2.0. arXiv preprint:1807.01069 (2018)
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: IEEE European Symposium on Security and Privacy (EuroS&P) (2016)
Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems (2019)
Peng, X., Huang, W., Shi, Z.: Adversarial attack against dos intrusion detection: an improved boundary-based method. In: IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI) (2019)
Piplai, A., Chukkapalli, S.S.L., Joshi, A.: Nattack! adversarial attacks to bypass a gan based classifier trained to detect network intrusion. arXiv preprint:2002.08527 (2020)
Rigaki, M., Elragal, A.: Adversarial deep learning against intrusion detection classifiers. In: NATO IST-152 Workshop on Intelligent Autonomous Agents for Cyber Defence and Resilience (2017)
Ring, M., Wunderlich, S., Scheuring, D., Landes, D., Hotho, A.: A survey of network-based intrusion detection data sets. Comput. Secur. 86, 147–167 (2019)
Rosenberg, I., Shabtai, A., Elovici, Y., Rokach, L.: Adversarial learning in the cyber security domain. arXiv preprint:2007.02407 (2020)
Sharafaldin, I., Lashkari, A.H., Ghorbani, A.A.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: 4th International Conference on Information Systems Security and Privacy (2018)
Szegedy, C., et al.: Intriguing properties of neural networks. arXiv preprint:1312.6199 (2013)
Tavallaee, M., Bagheri, E., Lu, W., Ghorbani, A.A.: A detailed analysis of the KDD cup 99 data set. In: IEEE Symposium on Computational Intelligence for Security and Defense Applications (2009)
Vorobeychik, Y., Kantarcioglu, M.: Adversarial machine learning. Synthesis Lectures on Artificial Intelligence and Machine Learning (2018)
Wang, Z.: Deep learning-based intrusion detection with adversaries. IEEE Access (2018)
Warzyński, A., Kołaczek, G.: Intrusion detection systems vulnerability on adversarial examples. In: Innovations in Intelligent Systems and Applications (2018)
Yang, K., Liu, J., Zhang, C., Fang, Y.: Adversarial examples against the deep learning based network intrusion detection systems. In: IEEE Military Communications Conference (MILCOM) (2018)
Yuan, X., He, P., Zhu, Q., Li, X.: Adversarial examples: attacks and defenses for deep learning. IEEE Trans. Neural Netw. Learn. Syst. 30, 2805–2824 (2019)
Acknowledgment
This research was partly funded by the European Union’s Horizon 2020 research and innovation program under the Secure Collaborative Intelligent Industrial Automation (SeCoIIA) project, grant agreement No 871967 and IRT SystemX projects (Exploratory research and PFS).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Merzouk, M.A., Cuppens, F., Boulahia-Cuppens, N., Yaich, R. (2021). A Deeper Analysis of Adversarial Examples in Intrusion Detection. In: Garcia-Alfaro, J., Leneutre, J., Cuppens, N., Yaich, R. (eds) Risks and Security of Internet and Systems. CRiSIS 2020. Lecture Notes in Computer Science(), vol 12528. Springer, Cham. https://doi.org/10.1007/978-3-030-68887-5_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-68887-5_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-68886-8
Online ISBN: 978-3-030-68887-5
eBook Packages: Computer ScienceComputer Science (R0)