Abstract
Adversarial machine learning is an area of study that examines both the generation and detection of adversarial examples, which are inputs specially crafted to deceive classifiers, and has been extensively researched specifically in the area of image recognition, where humanly imperceptible modifications are performed on images that cause a classifier to perform incorrect predictions.
The main objective of this paper is to study the behavior of multiple state of the art machine learning algorithms in an adversarial context. To perform this study, six different classification algorithms were used on two datasets, NSL-KDD and CICIDS2017, and four adversarial attack techniques were implemented with multiple perturbation magnitudes. Furthermore, the effectiveness of training the models with adversaries to improve recognition is also tested. The results show that adversarial attacks successfully deteriorate the performance of all the classifiers between 13% and 40%, with the Denoising Autoencoder being the technique with highest resilience to attacks.
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References
Chollet, F., et al.: Keras. https://keras.io. Accessed June 2019
Kdd99 dataset (KDD Cup 1999 data). http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html. Accessed June 2019
NSL-KDD Dataset. https://www.unb.ca/cic/datasets/nsl.html. Accessed June 2019
Ring, M., et al.: A survey of network-based intrusion detection data sets (2019). 12 Authors Suppressed Due to Excessive Length https://doi.org/10.1016/j.cose.2019.06.005
Domingos, P.: The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World. Basic Books Inc., New York (2018)
Duddu, V.: A survey of adversarial machine learning in cyber warfare. Def. Sci. J. 68(4), 356–366 (2018)
Lin, Z., et al.: IDSGAN: generative adversarial networks for attack generation against intrusion detection (2018). arXiv:1809.02077
Papernot, N., et al.: Technical report on the CleverHans v2.1.0 adversarial examples library (2018). arXiv:1610.00768
Sharafaldin, I., et al.: Toward generating a new intrusion detection dataset and intrusion traffic characterization. In: 4th International Conference on Information Systems Security and Privacy (2018)
Wang, Z.: Deep learning-based intrusion detection with adversaries. IEEE Access 6, 38:367–38:384 (2018)
Frazão, I., Abreu, P.H., Cruz, T., Araújo, H., Simões, P.: Denial of service attacks: detecting the frailties of machine learning algorithms in the classification process. In: Luiijf, E., Žutautaitė, I., Hämmerli, B.M. (eds.) CRITIS 2018. LNCS, vol. 11260, pp. 230–235. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05849-4_19
Rigaki, M., et al.: Adversarial deep learning against intrusion detection classifiers. Master’s thesis, Information Security’s master dissertation, Luleå University of Technology (2017)
Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous systems (2016). arXiv:1603.04467v2
Carlini, N., et al.: Towards evaluating the robustness of neural networks (2016). arXiv:1608.04644
Dhanabal, L., Shantharajah, D.S.P.: A study on NSL-KDD dataset for intrusion detection system based on classification algorithms. Int. J. Adv. Res. Comp. Comm. Eng. 4(6), 446–452 (2015)
Goodfellow, I., et al.: Explaining and harnessing adversarial examples (2015). arXiv:1412.6572
Moosavi-Dezfooli, S., et al.: DeepFool: a simple and accurate method to fool deep neural networks (2015). arXiv:1511.04599
Papernot, N., et al.: The limitations of deep learning in adversarial settings (2015). arXiv:1511.07528
Zamani, M.: Machine learning techniques for intrusion detection (2013). arXiv:1312.2177
Huang, L., et al.: Adversarial machine learning. In: Proceedings of the 4th ACM Workshop on Security and Artificial Intelligence, Illinois, USA, Chicago, pp. 43–58 (2011)
Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Kemmerer, R.A.: Cybersecurity. In: 25th International Conference on Software Engineering, pp. 705–715 (2003)
Acknowledgements
This work was supported by the ATENA European H2020 Project (H2020-DS-2015-1 Project 700581).
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Martins, N., Cruz, J.M., Cruz, T., Abreu, P.H. (2019). Analyzing the Footprint of Classifiers in Adversarial Denial of Service Contexts. In: Moura Oliveira, P., Novais, P., Reis, L. (eds) Progress in Artificial Intelligence. EPIA 2019. Lecture Notes in Computer Science(), vol 11805. Springer, Cham. https://doi.org/10.1007/978-3-030-30244-3_22
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DOI: https://doi.org/10.1007/978-3-030-30244-3_22
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