Advertisement

MalJs: Lexical, Structural and Behavioral Analysis of Malicious JavaScripts Using Ensemble Classifier

  • Surendran KEmail author
  • Prabaharan Poornachandran
  • Aravind Ashok Nair
  • Srinath N
  • Ysudhir Kumar
  • Hrudya P
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 625)

Abstract

Over the past few years javascript has grown up and revolutionized the web by allowing user defined scripts to run inside a web browser. The application of javascript ranges from providing beautiful visualization to performing complex data analytics and modeling machine learning algorithms. However javascript are also widely being used as a channel to execute malicious activities by means of redirection, drive-by-download, vulnerability exploitation and many more in the client side. In this paper we analyze the lexical, structural and behavior characteristics of javascript code to identify malicious javascript in the wild. Performance evaluation results show that our approach achieves better accuracy, with very small false positive and false negative ratios.

References

  1. 1.
    Hao, Y., et al.: JavaScript malicious codes analysis based on naive bayes classification. In: 2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC). IEEE (2014)Google Scholar
  2. 2.
    Santos, I., et al.: N-grams-based file signatures for malware detection. ICEIS 2(9), 317–320 (2009)Google Scholar
  3. 3.
    Wang, W-H, et al.: A static malicious javascript detection using SVM. In: Proceedings of the International Conference on Computer Science and Electronics Engineering, vol. 40 (2013)Google Scholar
  4. 4.
    Egele, M., Wurzinger, P., Kruegel, C., Kirda, E.: Defending browsers against drive-by downloads: mitigating heap-spraying code injection attacks. In: Flegel, U., Bruschi, D. (eds.) DIMVA 2009. LNCS, vol. 5587, pp. 88–106. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Choi, Y., Kim, T., Choi, S., Lee, C.: Automatic detection for javascript obfuscation attacks in web pages through string pattern analysis. In: Lee, Y.-h., Kim, T.-h., Fang, W.-c., Ślęzak, D. (eds.) FGIT 2009. LNCS, vol. 5899, pp. 160–172. Springer, Heidelberg (2009)Google Scholar
  6. 6.
    Phung, P.H., et al.: Between worlds: securing mixed javascript/actionscript multi-party web content. In: IEEE Transactions on Dependable and Secure Computing, 12 April 2015, pp. 443–457 (2015)Google Scholar
  7. 7.
    Darling, M., et al.: A lexical approach for classifying malicious URLs. In: 2015 International Conference on High Performance Computing and Simulation (HPCS). IEEE (2015)Google Scholar
  8. 8.
    Fraiwan, M., et al.: Analysis and identification of malicious javascript code. Inf. Secur. J. A Global Perspect. 1–11 (2012)Google Scholar
  9. 9.
    Fang, Z., et al.: A half-dynamic classification method on obfuscated malicious JavaScript detection. Int. J. Secur. Appl. 9(6), 251–262 (2015)Google Scholar
  10. 10.
    Provos, N., Mavrommatis, P., Rajab, M.A., Monrose, F.: All your iframes point to us (2008)Google Scholar
  11. 11.
    Kim, B.-I., Im, C.-T., Jung, H.-C.: Suspicious malicious web site detection with strength analysis of a javascript obfuscation. Int. J. Adv. Sci. Technol. 26, 19–32 (2011)Google Scholar
  12. 12.
    Choi, J., et al.: Efficient malicious code detection using n-gram analysis and SVM. In: 2011 14th International Conference on Network-Based Information Systems (NBiS). IEEE, 2011Google Scholar
  13. 13.
    Chong, C., Liu, D., Lee, W.: Malicious URL detectionGoogle Scholar
  14. 14.
    Provos, N., et al.: The ghost in the browser: analysis of web-based malware. In: HotBots 2007, p. 4 (2007)Google Scholar
  15. 15.
    Mutton, P.: EBay scripting flaws being actively exploited by fraudsters. Netcraft, 18 Feburary 2016. Web, 4 June 2016. http://news.netcraft.com/archives/2016/02/18/ebay-scripting-flaws-being-actively-exploited-by-fraudsters.html
  16. 16.
    Christodorescu, M., et al.: Semantics-aware malware detection. In: 2005 IEEE Symposium on IEEE Security and Privacy (2005)Google Scholar
  17. 17.
    Cova, M., Kruegel, C., Vigna, G.: Detection and analysis of drive-by-download attacks and malicious JavaScript code. In: Proceedings of the 19th international conference on World wide web. ACM (2010)Google Scholar
  18. 18.
    Hallaraker, O., Vigna, G.: Detecting malicious javascript code in mozilla. In: Proceedings. 10th IEEE International Conference on Engineering of Complex Computer Systems, ICECCS 2005. IEEE (2005)Google Scholar
  19. 19.
    Hedin, D., Sabelfeld, A.: Information-flow security for a core of JavaScript. In: 2012 IEEE 25th Computer Security Foundations Symposium (CSF). IEEE (2012)Google Scholar
  20. 20.
    Feinstein, B., Peck, D., SecureWorks, Inc.: Caffeine monkey: Automated collection, detection and analysis of malicious javascript. Black Hat USA 2007 (2007)Google Scholar
  21. 21.
    Li, Z., et al.: Knowing your enemy: understanding and detecting malicious web advertising. In: Proceedings of the 2012 ACM Conference on Computer and Communications Security. ACM (2012)Google Scholar
  22. 22.
    Likarish, P., Jung, E., Jo, I.: Obfuscated malicious javascript detection using classification techniques. In: MALWARE (2009)Google Scholar
  23. 23.
    Seifert, C., Welch, I., Komisarczuk, P.: Identification of malicious web pages with static heuristics. In: Australasian Telecommunication Networks and Applications Conference. ATNAC 2008. IEEE (2008)Google Scholar
  24. 24.
    Zarras, A., et al.: The dark alleys of madison avenue: understanding malicious advertisements. In: Proceedings of the 2014 Conference on Internet Measurement Conference. ACM (2014)Google Scholar
  25. 25.
    Xu, W., Zhang, F., Zhu, S.: JStill: mostly static detection of obfuscated malicious JavaScript code. In: Proceedings of the Third ACM Conference on Data and Application Security and Privacy. ACM (2013)Google Scholar
  26. 26.
    Canali, D., et al.: Prophiler: a fast filter for the large-scale detection of malicious web pages. In: Proceedings of the 20th International Conference on World Wide Web. ACM (2011)Google Scholar
  27. 27.
    Jim, T., Swamy, N., Hicks, M.: Defeating script injection attacks with browser-enforced embedded policies. In: Proceedings of the 16th International Conference on World Wide Web. ACM (2007)Google Scholar
  28. 28.
    Top Sites. Alexa Top 500 Global Sites. Web, 4 June 2016. http://www.alexa.com/topsites
  29. 29.
    Malware Domain List. Web. 4 June 2016. https://www.malwaredomainlist.com/mdl.php
  30. 30.
    Zdrnja, B., Brownlee, N., Wessels, D.: Passive monitoring of DNS anomalies. In: Hämmerli, B.M., Sommer, R. (eds.) DIMVA 2007. LNCS, vol. 4579, pp. 129–139. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  31. 31.
    Bilge, L., et al. EXPOSURE: finding malicious domains using passive DNS analysis. In: NDSS (2011)Google Scholar
  32. 32.
    Antonakakis, M., et al.: Building a dynamic reputation system for DNS. In: USENIX Security Symposium (2010)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2016

Authors and Affiliations

  • Surendran K
    • 1
    Email author
  • Prabaharan Poornachandran
    • 1
  • Aravind Ashok Nair
    • 1
  • Srinath N
    • 1
  • Ysudhir Kumar
    • 1
  • Hrudya P
    • 1
  1. 1.Amrita Center for Cyber Security, Amrita Vishwa VidyapeethamAmrita UniversityKollamIndia

Personalised recommendations