Adaptive Semantics-Aware Malware Classification
| Autor: | Bojan Kolosnjaji, Tamas K. Lengyel, George D. Webster, Claudia Eckert, Apostolis Zarras |
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Topic model
business.industry Computer science 020207 software engineering 02 engineering and technology Machine learning computer.software_genre Semantics Automation Class (biology) ComputingMethodologies_PATTERNRECOGNITION 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering Malware Data mining Artificial intelligence Malware analysis business Heuristics computer |
| Zdroj: | Delft University of Technology Proceedings of the 13th Conference on Detection of Intrusions and Malware Vulnerability Assessment (DIMVA) Detection of Intrusions and Malware, and Vulnerability Assessment ISBN: 9783319406664 DIMVA |
| Popis: | Automatic malware classification is an essential improvement over the widely-deployed detection procedures using manual signatures or heuristics. Although there exists an abundance of methods for collecting static and behavioral malware data, there is a lack of adequate tools for analysis based on these collected features. Machine learning is a statistical solution to the automatic classification of malware variants based on heterogeneous information gathered by investigating malware code and behavioral traces. However, the recent increase in variety of malware instances requires further development of effective and scalable automation for malware classification and analysis processes. In this paper, we investigate the topic modeling approaches as semantics-aware solutions to the classification of malware based on logs from dynamic malware analysis. We combine results of static and dynamic analysis to increase the reliability of inferred class labels. We utilize a semi-supervised learning architecture to make use of unlabeled data in classification. Using a nonparametric machine learning approach to topic modeling we design and implement a scalable solution while maintaining advantages of semantics-aware analysis. The outcomes of our experiments reveal that our approach brings a new and improved solution to the reoccurring problems in malware classification and analysis. |
| Databáze: | OpenAIRE |
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