Malware algorithm classification method based on big data analysis
| Autor: | Jingling Zhao, Shilei Chen, Mengchen Cao, Baojiang Cui |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
060201 languages & linguistics
Cyber-collection Network security business.industry Computer science Computer Networks and Communications Feature extraction Big data 06 humanities and the arts 02 engineering and technology computer.software_genre Cryptovirology Software security assurance 0602 languages and literature 0202 electrical engineering electronic engineering information engineering Malware 020201 artificial intelligence & image processing Malware analysis business Algorithm computer Software |
| Zdroj: | International Journal of Web and Grid Services. 13:112 |
| ISSN: | 1741-1114 1741-1106 |
| DOI: | 10.1504/ijwgs.2017.082077 |
| Popis: | Internet technology has greatly increased the number of malware attacks on networks. Consequently, it has also elevated the importance of automatic malware detection and classification technology based on big data analysis in the field of information security. This paper presents a new method for classifying malware algorithms that exhibits both high accuracy and high coverage. The method combines big data analysis with software security technologies such as feature extraction, machine learning, binary instrumentation and dynamic instruction flow analysis to achieve automated classification of malware algorithms. 20 classification experiments prove the correctness of the method. We also discuss future directions for improving the method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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