Advertisement

HDM-Analyser: a hybrid analysis approach based on data mining techniques for malware detection

  • Mojtaba Eskandari
  • Zeinab Khorshidpour
  • Sattar Hashemi
Original Paper

Abstract

Today’s security threats like malware are more sophisticated and targeted than ever, and they are growing at an unprecedented rate. To deal with them, various approaches are introduced. One of them is Signature-based detection, which is an effective method and widely used to detect malware; however, there is a substantial problem in detecting new instances. In other words, it is solely useful for the second malware attack. Due to the rapid proliferation of malware and the desperate need for human effort to extract some kinds of signature, this approach is a tedious solution; thus, an intelligent malware detection system is required to deal with new malware threats. Most of intelligent detection systems utilise some data mining techniques in order to distinguish malware from sane programs. One of the pivotal phases of these systems is extracting features from malware samples and benign ones in order to make at least a learning model. This phase is called “Malware Analysis” which plays a significant role in these systems. Since API call sequence is an effective feature for realising unknown malware, this paper is focused on extracting this feature from executable files. There are two major kinds of approach to analyse an executable file. The first type of analysis is “Static Analysis” which analyses a program in source code level. The second one is “Dynamic Analysis” that extracts features by observing program’s activities such as system requests during its execution time. Static analysis has to traverse the program’s execution path in order to find called APIs. Because it does not have sufficient information about decision making points in the given executable file, it is not able to extract the real sequence of called APIs. Although dynamic analysis does not have this drawback, it suffers from execution overhead. Thus, the feature extraction phase takes noticeable time. In this paper, a novel hybrid approach, HDM-Analyser, is presented which takes advantages of dynamic and static analysis methods for rising speed while preserving the accuracy in a reasonable level. HDM-Analyser is able to predict the majority of decision making points by utilising the statistical information which is gathered by dynamic analysis; therefore, there is no execution overhead. The main contribution of this paper is taking accuracy advantage of the dynamic analysis and incorporating it into static analysis in order to augment the accuracy of static analysis. In fact, the execution overhead has been tolerated in learning phase; thus, it does not impose on feature extraction phase which is performed in scanning operation. The experimental results demonstrate that HDM-Analyser attains better overall accuracy and time complexity than static and dynamic analysis methods.

Keywords

Application Program Interface Control Flow Graph Branch Node Executable File Dynamic Analysis Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Abou-Assaleh, T., Cercone, N., Keselj, V., Sweidan, R.: Detection of new malicious code using n-grams signatures. In: Proceedings of Second Annual Conference on Privacy, Security and Trust, pp. 193–196. Citeseer (2004)Google Scholar
  2. 2.
    Bayer, U., Kruegel, C., Kirda, E.: Ttanalyze: A tool for analyzing malware. In: 15th European Institute for Computer Antivirus Research (EICAR 2006) Annual Conference. Citeseer (2006)Google Scholar
  3. 3.
    Bergeron, J., Debbabi, M., Desharnais, J., Erhioui, M., Lavoie, Y., Tawbi, N.: Static detection of malicious code in executable programs. Int. J. Req. Eng. 2001, 184–189 (2001)Google Scholar
  4. 4.
    Bergeron, J., Debbabi, M., Desharnais, J., Ktari, B., Salois, M., Tawbi, N., Charpentier, R., Patry, M.: Detection of malicious code in cots software: A short survey. In: First International Software Assurance Certification Conference (ISACC99) (1999)Google Scholar
  5. 5.
    Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)CrossRefzbMATHGoogle Scholar
  6. 6.
    Cleary, J., Trigg, L.: K*: An instance-based learner using an entropic distance measure. In: Machine Learning-International Workshop Then Conference-, pp. 108–114. Citeseer (1995)Google Scholar
  7. 7.
    Dietterich, T.: An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Mach. Learn. 40(2), 139–157 (2000)CrossRefGoogle Scholar
  8. 8.
    Duds, R., Hart, P.: Pattern Classification and Scene Analysis. Wiley, New York (1973)Google Scholar
  9. 9.
  10. 10.
    Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Mach. Learn. 29(2), 131–163 (1997)CrossRefzbMATHGoogle Scholar
  11. 11.
    Holmes, G., Donkin, A., Witten, I.: Weka: A machine learning workbench. In: Intelligent Information Systems, 1994. Proceedings of the 1994 Second Australian and New Zealand Conference on, pp. 357–361. IEEE (1994)Google Scholar
  12. 12.
    Iba, W., Langley, P.: Induction of one-level decision trees. In: Proceedings of the Ninth International Conference on, Machine Learning, pp. 233–240 (1992)Google Scholar
  13. 13.
    Idika, N., Mathur, A.: A survey of malware detection techniques. Purdue University (2007)Google Scholar
  14. 14.
    Langley, P., Iba, W., Thompson, K.: An analysis of bayesian classifiers. In: Proceedings of the National Conference on Artificial Intelligence, pp. 223–223. Wiley, Hoboken (1992)Google Scholar
  15. 15.
    Lewis, D.: Naive (bayes) at forty: The independence assumption in information retrieval. Machine Learning: ECML-98, pp. 4–15 (1998)Google Scholar
  16. 16.
    Orenstein, D.: Quickstudy: Application programming interface (api) (2000)Google Scholar
  17. 17.
    Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, Los Altos (1988)Google Scholar
  18. 18.
    Picard, R., Cook, R.: Cross-validation of regression models. J. Am. Stat. Assoc., 575–583 (1984)Google Scholar
  19. 19.
    Platt, J.: 12 fast training of support vector machines using sequential minimal, optimization (1998)Google Scholar
  20. 20.
    Rabek, J., Khazan, R., Lewandowski, S., Cunningham, R.: Detection of injected, dynamically generated, and obfuscated malicious code. In: Proceedings of the 2003 ACM workshop on Rapid malcode, pp. 76–82. ACM (2003)Google Scholar
  21. 21.
    Roundy, K., Miller, B.: Hybrid analysis and control of malware. In: Recent Advances in Intrusion Detection, pp. 317–338. Springer, Berlin (2010) Google Scholar
  22. 22.
    Sekar, R., Bowen, T., Segal, M.: On preventing intrusions by process behavior monitoring. In: USENIX Intrusion Detection, Workshop, vol. 1999 (1999)Google Scholar
  23. 23.
    Siddiqui, M.: Data mining methods for malware detection. ProQuest (2008)Google Scholar
  24. 24.
    Sung, A., Xu, J., Chavez, P., Mukkamala, S.: Static analyzer of vicious executables (save). In: Computer Security Applications Conference, 2004. 20th Annual, pp. 326–334. IEEE (2004)Google Scholar
  25. 25.
    Szor, P.: The Art of Computer Virus Research and Defense. Addison-Wesley Professional, Reading (2005)Google Scholar
  26. 26.
    Tzermias, Z., Sykiotakis, G., Polychronakis, M., Markatos, E.: Combining static and dynamic analysis for the detection of malicious documents. In: Proceedings of the Fourth European Workshop on System Security, p. 4. ACM (2011)Google Scholar
  27. 27.
    Wagner, D., Dean, R.: Intrusion detection via static analysis. In: Security and Privacy, 2001. S &P 2001. Proceedings. 2001 IEEE Symposium on, pp. 156–168. IEEE (2001)Google Scholar
  28. 28.
    Xu, J., Sung, A., Chavez, P.: Mukkamala, S.: Polymorphic malicious executable scanner by api sequence analysis (2004)Google Scholar
  29. 29.
    Ye, Y., Wang, D., Li, T., Ye, D.: Imds: Intelligent malware detection system. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1043–1047. ACM (2007)Google Scholar

Copyright information

© Springer-Verlag France 2013

Authors and Affiliations

  • Mojtaba Eskandari
    • 1
  • Zeinab Khorshidpour
    • 1
  • Sattar Hashemi
    • 1
  1. 1.Department of Computer Science and EngineeringShiraz UniversityShirazIran

Personalised recommendations