Abstract
Anomaly payload detection looks for payloads that deviate from a predefined model of normality. Defining normality requires an intelligent approach. Machine learning algorithms have been widely applied to build classifiers that distinguish normal from anomalous activity. These algorithms construct vectors of features extracted from raw payloads of a given dataset and train the classifier with them. The success of the detection highly depends on the potential of the training dataset to properly represent network traffic. In this paper we show that an adversary knowing the distribution of the dataset and the specific feature construction method may generate attack vectors evading the classifier. Particularly, in the case the classifier uses a simple feature construction method based on 1-grams, getting real-world payloads to evade the classifier is feasible.We present experimental results regarding fourwell-known classification algorithms, namely,C4.5, CART, SupportVector Machines (SVM) and MultiLayer Perceptron (MLP).
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References
The HTTP dataset CSIC 2010 (2010)
Ateniese, G., Felici, G., Mancini, L.V., Spognardi, A., Villani, A., Vitali, D.: Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers. arXiv:1306.4447 (2013)
Biggio, B., Fumera, G., Roli, F.: Security evaluation of pattern classifiers under attack. IEEE Transactions on Knowledge and Data Engineering 99(PrePrints), 1 (2013)
Gu, G., Fogla, P., Dagon, D., Lee, W., Skorić, B.: Measuring Intrusion Detection Capability: an Information-theoretic Approach. In: ACM Symposium on Information, Computer and Communications Security, pp. 90–101. ACM, New York (2006)
Huang, L., Joseph, A.D., Nelson, B., Rubinstein, B.I., Tygar, J.D.: Adversarial machine learning. In: Workshop on Security and Artificial Intelligence, pp. 43–58. ACM, NY (2011)
Nguyen, H.T., Torrano-Gimenez, C., Alvarez, G., Franke, K., Petrović, S.: Enhancing the effectiveness of web application firewalls by generic feature selection. Logic Journal of IGPL 21(4), 560–570 (2013)
Pastrana, S., Mitrokotsa, A., Orfila, A., Peris-Lopez, P.: Evaluation of classification algorithms for intrusion detection in MANETs. Knowledge-Based Systems 36, 217–225 (2012)
Torrano-Gimenez, C., Nguyen, H.T., Alvarez, G., Franke, K.: Combining expert knowledge with automatic feature extraction for reliable web attack detection. Security and Communication Networks (2012)
Tsai, C.-F., Hsu, Y.-F., Lin, C.-Y., Lin, W.-Y.: Intrusion detection by machine learning: A review. Expert Systems with Applications 36(10), 11994–12000 (2009)
Wang, K., Parekh, J.J., Stolfo, S.J.: Anagram: A content anomaly detector resistant to mimicry attack. In: Zamboni, D., Kruegel, C. (eds.) RAID 2006. LNCS, vol. 4219, pp. 226–248. Springer, Heidelberg (2006)
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Pastrana, S., Torrano-Gimenez, C., Nguyen, H.T., Orfila, A. (2015). Anomalous Web Payload Detection: Evaluating the Resilience of 1-Grams Based Classifiers. In: Camacho, D., Braubach, L., Venticinque, S., Badica, C. (eds) Intelligent Distributed Computing VIII. Studies in Computational Intelligence, vol 570. Springer, Cham. https://doi.org/10.1007/978-3-319-10422-5_21
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DOI: https://doi.org/10.1007/978-3-319-10422-5_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10421-8
Online ISBN: 978-3-319-10422-5
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