The Effect of Attacker on three of Sampling Strategies in Active Learning

Ghofran M. Alqaralleh*, Mohammad A. Alshraideh**, Ali Rodan***
*_***Department of Computer Science, University of Jordan, Amman, Jordan.
Periodicity:April - June'2019


Due to the importance of active learning, it is used in a lot of sensitive applications. But, these applications found in the environments where there are adversaries act to limit or prevent an accurate performance and destroy the normal activity of the application system. The active learning is vulnerable in the sampling stage in an adversarial environment such as the application of intrusion detection system, and attempt to sabotage the model at this stage is the guarantee for the failure or sabotage the work of the model and inaccuracy. The aim of the attacker is to maximally increase the learned model risk. In the sampling stage, the representative or most informative instances will be selected from unlabeled data to label based on the sampling strategy, but these unlabeled data not checked before being offered to the selecting process. So, the attacker will impact this stage to misguide the active learner through polluted the unlabeled instances. The contribution to this paper is studying the effect of the attacker on the strategies that used in the selection of most informative instances in active learning for network intrusion detection and whether the effect varies depending on the strategy that used. The experimental results showed that the expected model change strategy not significantly affected by the attack compared with other strategies.


Active Learning. Intrusion Detection System. Expected Model Change. Uncertainty Sampling. Query by Committee.

How to Cite this Article?

Alqaralleh, G. M., Alshraideh, M. A., & Alrodan, A. (2019). The Effect of Attacker on three of Sampling Strategies in Active Learning. i-manager's Journal on Software Engineering, 13(4), 1-9.


[1]. Alqaralleh, G., Alshraideh, A., & Alrodan, A. (2018, August). A comparison study between different sampling strategies for intrusion detection system of active learning model. Journal of Computer Science, 14(8), 1155-1173.
[2]. Barreno, M., Nelson, B., Sears, R., Joseph, A. D., & Tygar, J. D. (2006, March). Can machine learning be secure?. In Proceedings of the 2006 ACM Symposium on Information, Computer and Communications Security (pp. 16-25). ACM.
[3]. Barreno, M., Nelson, B., Joseph, A. D., & Tygar, J. D. (2010). The security of machine learning. Machine Learning, 81(2), 121-148.
[4]. Bloodgood, M. (2018, January). Support vector machine active learning algorithms with query-bycommittee versus closest-to-hyperplane selection. In 2018 th IEEE 12 International Conference on Semantic Computing (ICSC) (pp. 148-155). IEEE.
[5]. Cohn, D., Atlas, L., & Ladner, R. (1994). Improving generalization with active learning. Machine Learning, 15(2), 201-221. Doi: 10.1007/BF00993277.
[6]. Gilad-Bachrach, R., Navot, A., & Tishby, N. (2006). Query by committee made real. In Advances in Neural Information Processing Systems (pp. 443-450).
[7]. Iglesias, J. E., Konukoglu, E., Montillo, A., Tu, Z., & Criminisi, A. (2011, July). Combining generative and discriminative models for semantic segmentation of CT scans via active learning. In Biennial International Conference on Information Processing in Medical Imaging (pp. 25-36). Springer, Berlin, Heidelberg. doi: 10.1007/978-3-642-22092-0_3.
[8]. Joshi, A. J., Porikli, F., & Papanikolopoulos, N. (2009, June). Multi-class active learning for image classification. In 2009 IEEE Conference on Computer Vision and Pattern Recognition (pp. 2372-2379). IEEE. DOI: 10.1109/CVPR. 2009.5206627.
[9]. Laskov, P., & Lippmann, R. (2010). Machine learning in adversarial environments. Machine Learning, 81(2), 115- 119.
[10]. Lewis, D. D., & Gale, W. A. (1994, August). A sequential algorithm for training text classifiers. In Proceedings of the 17th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 3-12). Springer-Verlag New York, Inc.
[11]. Long, J., Shelhamer, E., & Darrell, T. (2015). Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 3431-3440). doi: 10.1109/CVPR.2015.7298965.
[12]. Mamitsuka, N. A. H. (1998, July). Query learning strategies using boosting and bagging. In Machine Learning: Proceedings of the Fifteenth International Conference (ICML'98) (Vol. 1). Morgan Kaufmann Pub.
[13]. Miller, B., Kantchelian, A., Afroz, S., Bachwani, R., Dauber, E., Huang, L., & Tygar J. D. (2014, November). Adversarial active learning. In Proceedings of the 2014 Workshop on Artificial Intelligence and Security Workshop (pp.3-14). ACM.
[14]. O'Neill, J., Delany, S. J., & MacNamee, B. (2016). Model-Based and Model-Free Active Learning for Regression.
[15]. Roy, N., & McCallum, A. (2001). Toward optimal active learning through sampling estimation of error reduction. Proceedings of the 18th International Conference on Machine Learning, (pp. 441-448).
[16]. Settles, B. (2009). Active Learning Literature Survey, Computer Sciences. Tech. Rep. 1648. University of Wisconsin-Madison, Madison, Wis, USA.
[17]. Settles, B., Craven, M., & Ray, S. (2008). Multipleinstance active learning. In Advances in Neural Information Processing Systems (pp. 1289-1296).
[18]. Seung, H. S., Opper, M., & Sompolinsky, H. (1992, July). Query by committee. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory (pp. 287-294). ACM.
[19]. Sznitman, R., & Jedynak, B. (2010). Active testing for face detection and localization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(10), 1914- 1920. doi: 10.1109/TPAMI.2010.106.
[20]. Tong, S., & Koller, D. (2001). Support vector machine active learning with applications to text classification. Journal of Machine Learning Research, 2(Nov), 45-66.
[21]. Vezhnevets, A., Ferrari, V., & Buhmann, J. M. (2012, June). Weakly supervised structured output learning for semantic segmentation. In 2012 IEEE Conference on Computer Vision and Pattern Recognition (pp. 845-852). IEEE.
[22]. Yang, Y., Ma, Z., Nie, F., Chang, X., & Hauptmann, A. G. (2015). Multi-class active learning by uncertainty sampling with diversity maximization. International Journal of Computer Vision, 113(2), 113-127. doi: 10.1007/ s11263-014-0781-x
[23]. Zhao, W., Long, J., Yin, J., Cai, Z., & Xia, G. (2012, November). Sampling attack against active learning in adversarial environment. In International Conference on Modeling Decisions for Artificial Intelligence (pp. 222-233). Springer, Berlin, Heidelberg.

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