Oriental Journal of Computer Science and Technology

Open Access Open Access  Restricted Access Subscription Access

Wavelet Statistical Feature Based Malware Class Recognition and Classification using Supervised Learning Classifier


Affiliations
1 Department of Computer Science, Akkamahadevi Women’s University, Vijayapura, India
 

Malware is a malicious instructions which may harm to the unauthorized private access through internet. The types of malware are incresing day to day life, it is a challenging task for the antivius vendors to predict and caught on access time. This paper aims to design an automated analysis system for malware classes based on the features extracted by Discrete Wavelet Transformation (DWT) and then by applying four level decomposition of malware. The proposed system works in three stages, pre-processing, feature extraction and classification. In preprocessing, input image is normalized in to 256x256 by applying wavelet we are denoising the image which helps to enhance the image. In feature extraction, DWT is used to decompose image into four level. For classification the support vector machine (SVM) classifiers are used to discriminate the malware classes with statistical features extracted from level 4 decomposition of DWT such as Daubechies (db4), Coiflet (coif5) and Bi-orthogonal (bior 2.8). Among these wavelet features the db4 features effectively classify the malware class type with high accuracy 91.05% and 92.53% respectively on both dataset. The analysis of proposed method conducted on two dataset and the results are promising.

Keywords

Classification, Discrete Wavelet Transform, Feature Extraction, Malware Class, Texture and Pattern.
User
Notifications
Font Size

  • M. Labs. McAfee threats report: Second quarter (2015). Technical report, McAfee.

  • Symantec, Global Internet Security Threat Report, 2015.

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

  • M. Wagner, F. Fischer, R. Luh, A. Haberson, A. Rind, D. A. Keim, and W. Aigner, “ A Survey of Visualization Systems for Malware Analysis,” Euro graphics Conference on Visualization (EuroVis) (2015),Springer.

  • Nataraj L., Karthikeyan S., Jacob G., Manjunath B. S.,” Malware images: Visualization and automatic classification,” In Proc. 8th Int. Symp. Visualization for Cyber Security, VizSec (2011), ACM, pp. 4-7.

  • Tanuvir Singh, Fabio Di Troia, Visaggio Aaron Corrado, Thomas H. Austin. Mark Stamp1 2015, “Support vector machines and malware detection,” Journal Computer Virol Hack Tech, 2015.

  • Tantan Xu,” A file fragment classification method based on gray scale image,” Journal of computers, 9(8), 2014.

  • K Kyoung Soo Han, Jae Hyun Lim, Boojoong Kang, and Eul Gyu Im, “Malware Analysis Using Entropy Graphs,” Springer-Verlag Berlin Heidelberg, International Journal of Information Security, 2015.

  • Said Zainudeen Mohd Shaid, Mohd Aizaini Maarof, “Malware Behavior Image for Malware Variant Identification,” IEEE, International Symposium on Biometric and Security Technologies (ISBAST), 2014.

  • K Kong, D. and Yan, G. Discriminate, “Malware Distance Learning on Structural Information for Automated Malware Classification,” Proceedings of the ACM SIGMETRICS/International Conference on Measurement and Modelling of Computer Systems, 2013, pp. 347-348.

  • Acar Tamersoy, Kevin Roundy, Duen Horng Chau, Guilt by Association, “Large Scale Malware Detection by Mining File-relation Graphs,” In Proceedings of KDD 14, August 24-27, New York, NY, USA, 2014, pp: 1524-1533.

  • Aziz Makandar and Anita Patrot, “Computation Pre-Processing Techniques for Image Restoration,” International Journal of Computer Applications (0975-8887), 113(4), 2015.

  • Z. Wen, Y.Hu and W.Zhu. (2013),” Research on Feature Extraction of Halftone Image,” Journal of Software, 10, pp.2575-2580.

  • Y. Lan, Y.Zhang and H.Ren.(2013), “A Combinational K-View Based Algorithm for Texture Classification,” Journal of Software, 8, pp.218-227.

  • Smita Navali, Vijay Laxmi, Manoj Singh Gaur and Vinod P,” An efficient block-discriminate identification of packed malware,” Sadhana. 40(5), pp. 1435–1456, 2015.

  • Stavros D. Nikolopoulos Iosif Polenakis,”A graph-based model for malware detection and classification using system-call groups,” Journal Computer Virol Hack Tech.

  • Z. Wen, Y.Hu and W.Zhu. (2013),” Research on Feature Extraction of Halftone Image,” Journal of Software, 10, pp.2575-2580.

  • Y. Lan, Y.Zhang and H.Ren. (2013), “A Combinational K-View Based Algorithm for Texture Classification,” Journal of Software, 8, pp.218-227.

  • Acar Tamer soy, Kevin Roundy, Duen Horng Chau, Guilt by Association, “Large Scale Malware Detection by Mining File-relation Graphs,” In Proceedings of KDD 14, August 24-27, New York, NY, USA, 2014, pp: 1524-1533.

  • Aziz Makandar and Anita Patrot,”Malware Image Analysis and Classification using Support Vector Machine,” International Journal of Trends in Computer Science and Engineering,4(5), pp.01-03, 2015.http://www.warse.org/IJATCSE/static/pdf/Issue/icetem2015sp01.pdf

  • Aziz Makandar and Anita Patrot, “Overview of Malware Analysis and Detection,” International Journal of Computer Applications (0975-8887), National Conference on Knowledge, Innovation in Technology and Engineering (NCKITE 2015), pp.35-40.

  • Aziz Makandar and Anita Patrot, “Color Image Analysis and Contrast Stretching using Histogram Equalization,” International Journal of Advanced Information Science and Technology (IJAIST) ISSN 2319:2682, 27(27), ,pp.119-125, 2014.

  • Aziz Makandar and Anita Patrot,” Malware Image Analysis and Classification using Support Vector Machine,” International Conference on Emerging Trends in Engineering and Management (ICETEM 2015).

  • Aziz Makandar and Anita Patrot, “Texture Feature Extraction of Malware Gray scale image by using M-band Wavelet,” International Conference on Communication Networks and Signal Processing (ICCNSP 2015), Bangalore, India (December 3rd to 5th, 2015), Published by McGraHill publication.

  • Aziz Makandar and Anita Patrot,” Malware Analysis and Classification using Artificial Neural Network,” IEEE Xplorer, International Conference on Automation, Communication and Computing Technologies (ITACT 2015), December 22 and 23, Bangalore, IEEE Xplorer.

  • Aziz Makandar and Anita Patrot, “An approach to analysis of malware using Supervised Learning Classification”. International Conference on Recent Trends in Engineering, Science & Technology ICRTEST 2016. 25th–27th October 2016, IET Inspec.

  • Aziz Makandar and Anita Patrot, “Trojan Malware Image Pattern Classification,” International Conference on Cognition and Recognition, ICCR 2016,30-31,Mysore, December,2016, Springer.

  • Aziz Makandar and Anita Patrot,” Malware Class Recognition using Image Processing Techniques”, ICDMAI 2017, 24th to 26th Feb 2017, IEEE Xplorer, Puna.


Abstract Views: 264

PDF Views: 0




  • Wavelet Statistical Feature Based Malware Class Recognition and Classification using Supervised Learning Classifier

Abstract Views: 264  |  PDF Views: 0

Authors

Aziz Makandar
Department of Computer Science, Akkamahadevi Women’s University, Vijayapura, India
Anita Patrot
Department of Computer Science, Akkamahadevi Women’s University, Vijayapura, India

Abstract


Malware is a malicious instructions which may harm to the unauthorized private access through internet. The types of malware are incresing day to day life, it is a challenging task for the antivius vendors to predict and caught on access time. This paper aims to design an automated analysis system for malware classes based on the features extracted by Discrete Wavelet Transformation (DWT) and then by applying four level decomposition of malware. The proposed system works in three stages, pre-processing, feature extraction and classification. In preprocessing, input image is normalized in to 256x256 by applying wavelet we are denoising the image which helps to enhance the image. In feature extraction, DWT is used to decompose image into four level. For classification the support vector machine (SVM) classifiers are used to discriminate the malware classes with statistical features extracted from level 4 decomposition of DWT such as Daubechies (db4), Coiflet (coif5) and Bi-orthogonal (bior 2.8). Among these wavelet features the db4 features effectively classify the malware class type with high accuracy 91.05% and 92.53% respectively on both dataset. The analysis of proposed method conducted on two dataset and the results are promising.

Keywords


Classification, Discrete Wavelet Transform, Feature Extraction, Malware Class, Texture and Pattern.

References





DOI: https://doi.org/10.13005/ojcst%2F10.02.20