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  1. Proceedings of the Asian Internet Engineeering Conference (AINTEC '12)
  2. Malware classification based on extracted API sequences using static analysis
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On incentive-based inter-domain caching for content delivery in future internet architectures
Towards a pluralist internet using a virtual machine server for network customization
Delay tolerant network based e-mail system using trains
Data collection in heterogeneous wireless sensor networks
Malware classification based on extracted API sequences using static analysis
Fairness among high speed and traditional TCP under different queue management mechanisms
Congestion control scheme of Compound TCP+ in wireless LANs
External device session initiation framework using mobile phone connection information
Dissecting dissemination in VANETs
Improving an SVD-based combination strategy of anomaly detectors for traffic labelling
Validation of session layer protocols and API in ZNA

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Malware classification based on extracted API sequences using static analysis

Content Provider ACM Digital Library
Author Wasaki, Katsumi Iwamoto, Kazuki
Abstract In this paper, we propose a highly accurate, automatic malware-classification method, which extracts features by conducting static analysis of malware samples and the structure of malware source code. In the proposed extraction method, the presence and absence of particular pairs of consecutive Application Program Interface function calls (APIs) in the API-sequence graph are compared with those in the executable code for a sample within which malware features have been identified. To determine the degree of similarity between samples, Dice's coefficient is applied. To visualize the grouping of samples with similar features, we use hierarchical cluster analysis based on the extracted features. The results of the analysis are presented as a dendrogram with colored nodes for each family name. To evaluate the proposed method, we set up a malware-analysis system comprising a combination of disassembler, control-flow analyzer, API-sequence extractor, similarity calculator and hierarchical cluster analyzer. We acquired 4,684 malware samples, from 1,821 of which we successfully extracted API sequences to which we applied our proposed classification method. We found that the automatic hierarchical cluster analysis was processed rapidly, with significant clusters of variant groups obtained.
Starting Page 31
Ending Page 38
Page Count 8
File Format PDF
ISBN 9781450318143
DOI 10.1145/2402599.2402604
Language English
Publisher Association for Computing Machinery (ACM)
Publisher Date 2012-11-14
Publisher Place New York
Access Restriction Subscribed
Subject Keyword Api sequence Classification Static analysis Control-flow analysis Malware
Content Type Text
Resource Type Article
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