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Makandar, Aziz
- Wavelet Statistical Feature Based Malware Class Recognition and Classification using Supervised Learning Classifier
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Authors
Affiliations
1 Department of Computer Science, Akkamahadevi Women’s University, Vijayapura, IN
1 Department of Computer Science, Akkamahadevi Women’s University, Vijayapura, IN
Source
Oriental Journal of Computer Science and Technology, Vol 10, No 2 (2017), Pagination: 400-406Abstract
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.Full Text
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- 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.
- Land Use Land Cover Study of Sentinel-2A and Landsat-5 Images Using NDVI and Supervised Classification Techniques
Abstract Views :129 |
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Authors
Affiliations
1 Department of Computer Science, Karnataka State Akkamahadevi Women’s University, IN
1 Department of Computer Science, Karnataka State Akkamahadevi Women’s University, IN
Source
ICTACT Journal on Image and Video Processing, Vol 12, No 2 (2021), Pagination: 2582-2587Abstract
Land Use Land Cover (LULC) change monitoring plays very significant role in planning, policy making, management programs required for development activities at regional levels of any country. This study is an attempt to monitor LULC change of Vijayapura taluk, Karnataka, India for the period of 25 years from 1995 to 2021 using Remote Sensing (RS) and Geographic Information System (GIS). Satellite Images from Sentinel-2A MSI (Multispectral Imager), Landsat-5TM (Thematic Mapper) are used to generate LULC maps. Vegetation Change in the study area is computed using Normalized Difference Vegetation Index (NDVI) and results show that vegetation rate is increased from 0.6% in 1995 to 27.5% in 2021. Supervised Classification is carried out by using Maximum Likelihood Classification (MLC). 5 major classes considered for classification are namely: Waterbodies, Cropland/Vegetation, Fallow Land, Built-up Area and Barren Land. ArcGIS software tool is used for implementing the proposed study. Google Earth Pro used for accuracy assessment which is done by taking ground truth values for corresponding Classifications. Results show that the proposed system is able to achieve 88.16% of overall accuracy.Keywords
Land Use Land Cover, Maximum Likelihood Classification, Normalized Difference Vegetation Index, Remote Sensing, Supervised Classification
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