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Dictionary Extraction and Detection of Algorithmically Generated Domain Names in Passive DNS Traffic

  • Mayana PereiraEmail author
  • Shaun Coleman
  • Bin Yu
  • Martine DeCock
  • Anderson Nascimento
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11050)

Abstract

Automatic detection of algorithmically generated domains (AGDs) is a crucial element for fighting Botnets. Modern AGD detection systems have benefited from the combination of powerful advanced machine learning algorithms and linguistic distinctions between legitimate domains and malicious AGDs. However, a more evolved class of AGDs misleads the aforementioned detection systems by generating domains based on wordlists (also called dictionaries). The resulting domains, Dictionary-AGDs, are seemingly benign to both human analysis and most of AGD detection methods that receive as input solely the domain itself. In this paper, we design and implement method called WordGraph for extracting dictionaries used by the Domain Generation Algorithms (DGAs) solely DNS traffic. Our result immediately gives us an efficient mechanism for detecting this elusive, new type of DGA, without any need for reverse engineering to extract dictionaries. Our experimental results on data from known Dictionary-AGDs show that our method can extract dictionary information that is embedded in the malware code even when the number of DGA domains is much smaller than that of legitimate domains, or when multiple dictionaries are present in the data. This allows our approach to detect Dictionary-AGDs in real traffic more accurately than state-of-the-art methods based on human defined features or featureless deep learning approaches.

Keywords

Malicious domain name Domain generation algorithm Dictionary-AGD Malware detection Machine learning 

References

  1. 1.
    Abbink, J., Doerr, C.: Popularity-based detection of domain generation algorithms. In: Proceedings of the 12th International Conference on Availability, Reliability and Security, p. 79. ACM (2017)Google Scholar
  2. 2.
    ALEXA: Top sites on the web (2017). http://alexa.com/topsites
  3. 3.
    Antonakakis, M., et al.: From throw-away traffic to bots: detecting the rise of DGA-based malware. In: 21st USENIX Security Symposium, pp. 24–24 (2012). http://dl.acm.org/citation.cfm?id=2362793.2362817
  4. 4.
    Barabosch, T., Wichmann, A., Leder, F., Gerhards-Padilla, E.: Automatic extraction of domain name generation algorithms from current malware. In: Proceedings of NATO Symposium IST-111 on Information Assurance and Cyber Defense (2012)Google Scholar
  5. 5.
    Bilge, L., Kirda, E., Kruegel, C., Balduzzi, M.: Exposure: finding malicious domains using passive DNS analysis. In: NDSS (2011)Google Scholar
  6. 6.
    Diestel, R.: Graph Theory. Graduate Texts in Mathematics, vol. 137. Springer, Heidelberg (2005)Google Scholar
  7. 7.
    Geffner, J.: End-to-end analysis of a domain generating algorithm malware family. Black Hat USA 2013 (2013)Google Scholar
  8. 8.
    Krishnan, S., Taylor, T., Monrose, F., McHugh, J.: Crossing the threshold: detecting network malfeasance via sequential hypothesis testing. In: 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pp. 1–12 (2013)Google Scholar
  9. 9.
    Lind, P.G., Gonzalez, M.C., Herrmann, H.J.: Cycles and clustering in bipartite networks. Phys. Rev. E 72(5), 056127 (2005)CrossRefGoogle Scholar
  10. 10.
    Lison, P., Mavroeidis, V.: Automatic detection of malware-generated domains with recurrent neural models. arXiv:1709.07102 (2017)
  11. 11.
    Ma, J., Saul, L.K., Savage, S., Voelker, G.M.: Beyond blacklists: learning to detect malicious web sites from suspicious URLs. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 1245–1254 (2009).  https://doi.org/10.1145/1557019.1557153
  12. 12.
    Mao, G., Zhang, N.: Analysis of average shortest-path length of scale-free network. J. Appl. Math. (2013). http://dx.doi.org/10.1155/2013/865643
  13. 13.
    McGrath, D.K., Gupta, M.: Behind phishing: an examination of phisher modi operandi. LEET 8, 4 (2008)Google Scholar
  14. 14.
    Mowbray, M., Hagen, J.: Finding domain-generation algorithms by looking at length distribution. In: 25th IEEE International Symposium on Software Reliability Engineering Workshops, ISSRE Workshops, pp. 395–400 (2014).  https://doi.org/10.1109/ISSREW.2014.20
  15. 15.
    Plohmann, D., Yakdan, K., Klatt, M., Bader, J., Gerhards-Padilla, E.: A comprehensive measurement study of domain generating malware. In: 25th USENIX Security Symposium, pp. 263–278 (2016)Google Scholar
  16. 16.
    Saxe, J., Berlin, K.: eXpose: a character-level convolutional neural network with embeddings for detecting malicious URLs, file paths and registry keys. arXiv:1702.08568 (2017)
  17. 17.
    Schiavoni, S., Maggi, F., Cavallaro, L., Zanero, S.: Phoenix: DGA-based botnet tracking and intelligence. In: Dietrich, S. (ed.) DIMVA 2014. LNCS, vol. 8550, pp. 192–211. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-08509-8_11CrossRefGoogle Scholar
  18. 18.
    Skuratovich, S.: Matsnu technical report. Check Point Software Technologies Ltd. (2015). https://blog.checkpoint.com/wp-content/uploads/2015/07/matsnu-malwareid-technical-brief.pdf
  19. 19.
    Tran, D., Mac, H., Tong, V., Tran, H.A., Nguyen, L.G.: A LSTM based framework for handling multiclass imbalance in DGA botnet detection. Neurocomputing 275, 2401–2413 (2018)CrossRefGoogle Scholar
  20. 20.
    Woodbridge, J., Anderson, H.S., Ahuja, A., Grant, D.: Predicting domain generation algorithms with long short-term memory networks. arXiv:1611.00791 (2016)
  21. 21.
    Yadav, S., Reddy, A.K.K., Reddy, A.L.N., Ranjan, S.: Detecting algorithmically generated malicious domain names. In: Proceedings of the 10th ACM SIGCOMM Conference on Internet Measurement, pp. 48–61 (2010).  https://doi.org/10.1145/1879141.1879148
  22. 22.
    Yu, B., Gray, D., Pan, J., De Cock, M., Nascimento, A.: Inline DGA detection with deep networks. In: Data Mining for Cyber Security, Proceedings of International Conference on Data Mining (ICDM 2017) Workshops, pp. 683–692 (2017)Google Scholar
  23. 23.
    Yu, B., Pan, J., Hu, J., Nascimento, A., De Cock, M.: Character level based detection of DGA domain names. In: Proceedings of IJCNN at WCCI2018 (2018 IEEE World Congress on Computational Intelligence) (2018)Google Scholar
  24. 24.
    Yu, B., Smith, L., Threefoot, M.: Semi-supervised time series modeling for real-time flux domain detection on passive DNS traffic. In: Perner, P. (ed.) MLDM 2014. LNCS (LNAI), vol. 8556, pp. 258–271. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-08979-9_20CrossRefGoogle Scholar
  25. 25.
    Yu, B., Smith, L., Threefoot, M., Olumofin, F.: Behavior analysis based DNS tunneling detection with big data technologies. In: Proceedings of the International Conference on Internet of Things and Big Data, pp. 284–290 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Mayana Pereira
    • 1
    Email author
  • Shaun Coleman
    • 2
  • Bin Yu
    • 1
  • Martine DeCock
    • 2
  • Anderson Nascimento
    • 2
  1. 1.Infoblox Inc.Santa ClaraUSA
  2. 2.Institute of TechnologyUniversity of Washington TacomaTacomaUSA

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