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A Survey on Different Approaches for Malware Detection Using Machine Learning Techniques

  • S. Soja RaniEmail author
  • S. R. Reeja
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 39)

Abstract

Malwares are increasing in volume and variety, by posing a big threat to digital world and is one of the major alarms over the past few years for the security in industries. They can penetrate networks, steal confidential information from computers, bring down servers and can cripple infrastructures. Traditional Anti-Intrusion Detection/Intrusion prevention system and anti-virus softwares follow signature based methods which makes the detection of unknown or zero day malwares almost impossible. This issue can be solved by more sophisticated mechanisms in which, static and dynamic malware analysis can be used together with machine learning algorithms for classifying and detecting malware. Through this paper we present a survey on the different techniques for concealment and obfuscation used to make sophisticated malware as well as the different approaches used in malware detection and analysis.

Keywords

Malware analysis CyberSecurity Machine learning 

References

  1. 1.
    Digital Object Identifier: The effects of traditional anti-virus labels on malware detection using dynamic runtime opcode.  https://doi.org/10.1109/ACCESS.2017.2749538CrossRefGoogle Scholar
  2. 2.
    Beaucamps, P.: Advanced polymorphic techniques. Int. J. Comput. Sci. 2(3), 194–205 (2007)Google Scholar
  3. 3.
    Wong, W., Stamp, M.: Hunting for metamorphic engines. J. Comput. Virol. 2, 211229 (2006)CrossRefGoogle Scholar
  4. 4.
    Govindaraju, A.: Exhaustive statistical analysis for detection of metamorphic malware. [MS Project], San Jose State University, US (2010)Google Scholar
  5. 5.
    Wang, P., Wang, Y.-S.: Malware behavioural detection and vaccine development by using a support vector model classifier. J. Comput. Syst. Sci. 81, 1012–1026 (2015)CrossRefGoogle Scholar
  6. 6.
    Santos, I., Brezo, F., Ugarte-Pedrero, X., Bringas, P.G.: Opcode sequences as representation of executables for datamining-based unknown malware detection. Inf. Sci. 231, 64–82 (2013)CrossRefGoogle Scholar
  7. 7.
    Martín, A., Menéndez, H.D., Camacho, D.: MOCDroid: multi-objective evolutionary classifier for Android malware detection. Soft. Comput. 21, 7405–7415 (2017)CrossRefGoogle Scholar
  8. 8.
    Hellal, A., Romdhane, L.B.: Minimal contrast frequent pattern mining for malware detection. Comput. Secur. 62, 19–32 (2016)CrossRefGoogle Scholar
  9. 9.
    Fan, Y., Ye, Y., Chen, L.: Malicious sequential pattern mining for automatic malware detection. Expert Syst. Appl. 52, 16–25 (2016)CrossRefGoogle Scholar
  10. 10.
    Boujnouni, M.E., Jedra, M., Zahid, N.: New malware detection framework based on N-grams and support vector domain description. In: 2015 11th International Conference on Information Assurance and Security (IAS), pp. 123–128 (2015)Google Scholar
  11. 11.
    Ye, Y., Chen, L., Hou, S., Hardy, W., Li, X.: DeepAM: a heterogeneous deep learning framework for intelligent malware detection. Knowl. Inf. Syst. 54, 265–285 (2017)CrossRefGoogle Scholar
  12. 12.
    Bayer, U., Moser, A., Krugel, C., Kirda, E.: Dynamic analysis of malicious code. J. Comput. Virol. 2(1), 67–77 (2006)CrossRefGoogle Scholar
  13. 13.
    Willems, C., Holz, T., Freiling, F.: Toward automated dynamic malware analysis using CWSandbox. IEEESecur. Priv. 5(2), 32–39 (2007)CrossRefGoogle Scholar
  14. 14.
    Mohaisen, A., Alrawi, O., Mohaisen, M.: AMAL: high-fidelity, behavior-based automated malware analysis and classification. Comput. Secur. 52, 251–266 (2015)CrossRefGoogle Scholar
  15. 15.
    Norouzi, M., Souri, A., Samad Zamini, M.: A data mining classification approach for behavioral malware detection. J. Comput. Netw. Commun. 2016, 9 (2016)Google Scholar
  16. 16.
    Eskandari, M., Khorshidpour, Z., Hashemi, S.: HDM-analyser: a hybrid analysis approach based on data mining techniques for malware detection. J. Comput. Virol. Hacking Tech. 9, 77–93 (2013)CrossRefGoogle Scholar
  17. 17.
    Yuan, Z., Lu, Y., Xue, Y.: DroidDetector: android malware characterization and detection using deep learning. Tsinghua Sci. Technol. 21, 114–123 (2016)CrossRefGoogle Scholar
  18. 18.
    Dali, Z., Hao, J., Ying, Y., Wu, D., Weiyi, C.: DeepFlow: deep learning-based malware detection by mining Android application for abnormal usage of sensitive data. In: 2017 IEEE Symposium on Computers and Communications (ISCC), pp 438–443 (2017)Google Scholar
  19. 19.
    Ding, Y., Yuan, X., Tang, K., Xiao, X., Zhang, Y.: A fast malware detection algorithm based on objective-oriented association mining. Comput. Secur. 39(Part B), 315–324 (2013)CrossRefGoogle Scholar
  20. 20.
    Rehman, Z.-U., Khan, S.N., Muhammad, K., Lee, J.W., Lv, Z., Baik, S.W., Shah, P.A., Awan, K., Mehmood, I.: Machine learning assisted signature and heuristic-based detection of malwares in Android devices. Comput. Electr. Eng. 69, 828–841 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Dayananda Sagar UniversityBangaloreIndia

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