Abstract
Malware steals private information by randomly generating a large number of malicious domain names every day using domain generation algorithms (DGAs), which pose a great threat to our daily Internet activity. To improve recognition accuracy for these malicious domain names, this paper proposes a malicious domain name detection algorithm based on deep neural networks to capture the characteristics of malicious domain names. The resulting model is called a Discriminator based on Hierarchical Bidirectional Recurrent Neural Networks (D-HBiRNN).
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References
Hoque, N., Bhattacharyya, D.K., Kalita, J.K.: Botnet in DDoS attacks: trends and challenges. IEEE Commun. Surv. Tutor. 17(4), 2242–2270 (2015)
Rossow, C.: Amplification hell: revisiting network protocols for DDoS abuse. In: Proceedings 2014 Network and Distributed System Security Symposium. Internet Society, Reston, VA (2014). https://doi.org/10.14722/ndss.2014.23233
Thatte, G., Mitra, U., Heidemann, J.: Parametric methods for anomaly detection in aggregate traffic. IEEE/ACM Trans. Netw. 19(2), 512–525 (2011)
Graves, A.: Supervised Sequence Labelling with Recurrent Neural Networks, vol. 385. Springer, Berlin (2012). https://doi.org/10.1007/978-3-642-24797-2
Duffield, N., Haffner, P., Krishnamurthy, B., et al.: Rule-based anomaly detection on IP flows. In: INFOCOM, pp. 424–432. IEEE (2009)
Chen, T., Xu, S., Zhang, C.: Risk assessment method for network security based on intrusion detection system. Comput. Sci. 37(9), 94–96 (2010)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: International Conference on Neural Information Processing Systems, pp. 1097–1105. Curran Associates Inc. (2012)
He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: Computer Vision and Pattern Recognition, pp. 770–778. IEEE (2016)
Schuster, M., Paliwal, K.K.: Bidirectional recurrent neural networks. IEEE Trans. Signal Process 45(11), 2673–2681 (1997)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Netlab 360 Homepage. https://data.netlab.360.com/dga. Accessed 21 Sept 2018
Haddadi, F., Kayacik, H.G., Zincir-Heywood, A.N., Heywood, M.I.: Malicious automatically generated domain name detection using stateful-SBB. In: Esparcia-Alcázar, A.I. (ed.) EvoApplications 2013. LNCS, vol. 7835, pp. 529–539. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37192-9_53
Xiong, C., Li, P., Zhang, P., Liu, Q., Tan, J.: MIRD: trigram-based Malicious URL detection Implanted with Random Domain name recognition. In: Niu, W., et al. (eds.) ATIS 2015. CCIS, vol. 557, pp. 303–314. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48683-2_27
Jamdagni, A., Jamdagni, A., He, X., et al.: A system for denial-of-service attack detection based on multivariate correlation analysis. IEEE Trans. Parallel Distrib. Syst. 25(2), 447–456 (2014)
Buczak, A.L., Guven, E.: A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Commun. Surv. Tutor. 18(2), 1153–1176 (2017)
Thomas, K., Grier, C., Ma, J., et al.: Design and evaluation of a real-time URL spam filtering service. In: Security and Privacy, pp. 447–462. IEEE (2011)
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Yan, X., Cui, B., Li, J. (2018). Malicious Domain Name Recognition Based on Deep Neural Networks. In: Wang, G., Chen, J., Yang, L. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2018. Lecture Notes in Computer Science(), vol 11342. Springer, Cham. https://doi.org/10.1007/978-3-030-05345-1_43
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DOI: https://doi.org/10.1007/978-3-030-05345-1_43
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