Automated malware recognition method based on local neighborhood binary pattern

Malware recognition has been widely used in the literature. One of the malware recognition methods is the byte code based methods. These methods generally use image processing and machine learning methods together to recognize malware. In this article, a novel byte code based malware recognition met...

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Bibliographic Details
Published inMultimedia tools and applications Vol. 79; no. 37-38; pp. 27815 - 27832
Main Authors Tuncer, Turker, Ertam, Fatih, Dogan, Sengul
Format Journal Article
LanguageEnglish
Published New York Springer US 01.10.2020
Springer Nature B.V
Subjects
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Discriminant analysis
Feature extraction
Image processing
Machine learning
Malware
Multimedia Information Systems
Neighborhoods
Pattern recognition
Principal components analysis
Special Purpose and Application-Based Systems
Grayscale image processing
LNBP
Cyber security
Malware recognition
Machine learning
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Summary:Malware recognition has been widely used in the literature. One of the malware recognition methods is the byte code based methods. These methods generally use image processing and machine learning methods together to recognize malware. In this article, a novel byte code based malware recognition method is presented, and it consists of feature extraction using the proposed local neighborhood binary pattern (LNBP), feature concatenation, feature selection with neighborhood component analysis (NCA), feature reduction using principal component analysis (PCA) and classification using linear discriminant analysis. A heterogeneous and mostly used byte-based malware dataset (Maligm) was chosen to evaluate the performance of the proposed LNBP based recognition method. The best accuracy rate was equal to 89.40%. The proposed LNBP based method was also compared to the state-of-art deep learning methods, and it achieved a higher success rate than them. These results clearly demonstrate prove the success of the proposed LNBP based method.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-020-09376-6