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Proceedings of International Conference on Cognition and Recognition pp 253–262Cite as

Trojan Malware Image Pattern Classification

Part of the Lecture Notes in Networks and Systems book series (LNNS,volume 14)

Abstract

The malicious data’s are grouped into various types of viruses such as Trojan, Trojan downloader and so on. The cyber security issues are increasing day to day. It is a challenging task for the network security and antivirus designers. Trojan malware family has been used to detect the newly arrived malicious data to a known group. It is a well popular research problem to detect and classify the Trojan viruses. This research issue is solved using image processing techniques. To identify the texture patterns of malware images is tedious task, because of the similarities present in the various malware families. In this proposed algorithm Gabor wavelet is used for key of feature extraction method. The dataset Malimng consists of the 25 malware variant families for each class max 300–1000 samples are there. The experimental results are analyzed compared with two classifications such as KNN and SVM. The texture patter classification accuracy is improved and false positive rate is decreased. The KNN gives accuracy 89.11% and SVM gives 75.11%.

Keywords

  • Gabor wavelet
  • K-nearest neighbour
  • Malware
  • Support vector machine
  • Trojan
  • Texture analysis

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References

  1. M. Labs. McAfee threats report: second quarter (2015) Technical report, McAfee

    Google Scholar 

  2. Symantec (2015) Global internet security threat report

    Google Scholar 

  3. Malware-Wikipedia, the free encyclopedia. https://en.wikipedia.org/wiki/Malware

  4. Wagner M, Fischer F, Luh R, Haberson A, Rind A, Keim DA, Aigner W (2015) A survey of visualization systems for malware analysis. In: Eurographics conference on visualization (EuroVis), Springer

    Google Scholar 

  5. Nataraj L, Karthikeyan S, Jacob G, Manjunath BS (2011) Malware images: visualization and automatic classification. In: Proceedings of 8th international symposium visualization for cyber security, VizSec, ACM, pp 4–7

    Google Scholar 

  6. Nataraj L, Yegneswaran V, Porras P, Zhang J (2011) A comparative assessment of malware classification using binary texture analysis and dynamic analysis. In: Proceedings of 4th ACM workshop on security and artificial intelligence, AISec, pp 21–30

    Google Scholar 

  7. Makandar A, Patrot A (2015) Overview of malware analysis and detection. In: IJCA proceedings on national conference on knowledge, innovation in technology and engineering, NCKITE, 2015(1):35–40

    Google Scholar 

  8. Singh T, Di Troia F, Corrado VA, Austin TH, Stamp M (2015) Support vector machines and malware detection. J Comput Virol Hack Tech

    Google Scholar 

  9. Han KS, Lim JH, Kang B, EulGyuIm (2015) Malware analysis using entropy graphs. Int J Inform Secur

    Google Scholar 

  10. Makandar A, Patrot A (2015) Malware analysis and classification using artificial neural network. In: IEEE explorer international conference on automation, communication and computing technologies (ITACT 2015), Dec 22 and 23, Bangalore

    Google Scholar 

  11. Xu T et al (2014) A file fragment classification method based on gray scale image. J Comput 9(8)

    Google Scholar 

  12. Makandar A, Patrot A (2015) Malware image analysis and classification using support vector machine. Int J Trends Comput Sci Eng 4(5):01–03

    Google Scholar 

  13. Shaid SZM, Maarof MA (2017) Malware behavior image for malware variant identification. In: IEEE international symposium on biometric and security technologies (ISBAST)

    Google Scholar 

  14. Kong D, Yan G (2013) Discriminant malware distance learning on structural information for automated malware classification. In: Proceedings of the ACM SIGMETRICS/international conference on measurement and modeling of computer systems, 2013, pp 347–348

    Google Scholar 

  15. Tamersoy A, Roundy K, Chau DH (2014) Guilt by association: large scale malware detection by mining file-relation graphs. In Proceedings of KDD 14, Aug 24–27, New York, NY, USA, pp 1524–1533

    Google Scholar 

  16. Makandar A, Patrot A (2015) Computation pre-processing techniques for image restoration. Int J Comput Appl 113(4):11–17

    Google Scholar 

  17. Wen Z, Hu Y, Zhu W (2013) Research on feature extraction of halftone image. J Softw 10:2575–2580

    Google Scholar 

  18. Lan Y, Zhang Y, Ren H (2013) A combinational K-view based algorithm for texture classification. J Softw 8:218–227

    Google Scholar 

  19. Navali S, Laxmi V, Gaur MS, Vinod P (2015) An efficient block-discriminant identification of packed malware. Sadhana 40(5):1435–1456

    Google Scholar 

  20. Nikolopoulos SD, Polenakis I (2016) A graph-based model for malware detection and classification using system-call groups. J Comput Virol Hack Tech

    Google Scholar 

  21. Makandar A, Patrot A (2015) Texture feature extraction of malware gray scale image by using M-band wavelet. In: International conference on communication networks and signal processing (ICCNSP 2015), Bangalore, India, 3–5 Dec 2015, McGraHill Publication

    Google Scholar 

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Acknowledgements

This research work is funded by UGC under Rajiv Gandhi National Fellowship (RGNF) UGC Letter No: F1-17.1/2014-15/RGNF-2014-15-SC-KAR-69608, February, 2015, Karnataka, India.

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Authors and Affiliations

  1. Karnataka State Women’s University, Vijayapura, India

    Aziz Makandar & Anita Patrot

Authors
  1. Aziz Makandar
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  2. Anita Patrot
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Corresponding author

Correspondence to Anita Patrot .

Editor information

Editors and Affiliations

  1. Department of Studies in Computer Science, University of Mysore, Mysore, Karnataka, India

    Prof. Dr. D. S. Guru

  2. Department of Master of Computer Application, Maharaja Institute of Technology, Mysore, Karnataka, India

    Dr. T. Vasudev

  3. Department of Computer Science and Engineering, Maharaja Institute of Technology, Mysore, Karnataka, India

    Dr. H.K. Chethan

  4. Department of Information Science and Engineering, Maharaja Institute of Technology, Mysore, Karnataka, India

    Dr. Y.H. Sharath Kumar

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Makandar, A., Patrot, A. (2018). Trojan Malware Image Pattern Classification. In: Guru, D., Vasudev, T., Chethan, H., Kumar, Y. (eds) Proceedings of International Conference on Cognition and Recognition . Lecture Notes in Networks and Systems, vol 14. Springer, Singapore. https://doi.org/10.1007/978-981-10-5146-3_24

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  • DOI: https://doi.org/10.1007/978-981-10-5146-3_24

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