Feature Extraction using DLL/API Statistical Analysis and Malware Detection based on Machine Learning 


Vol. 43,  No. 4, pp. 730-739, Apr.  2018
10.7840/kics.2018.43.4.730


PDF
  Abstract

In recent years, more than hundreds of thousands of new and variant malicious codes have appeared in various forms, including Ransomware, Malicious code for direct money takeover is increasing. Static based analysis techniques are required to dispose of malicious codes rapidly while consuming a small number of resources. In this thesis, we want to detect malicious code by composing a quick and light-like feature using the Imported DLL and API among static analysis techniques. By Whole investigation through the Imported DLL / API of more than 80,000 files, Identifying the Trends of Malicious Codes and Normal files, It provides the possibility to compare the machine learning results to verify the Feature Selection Policy and to link it other malicious code analysis, It is expected to contribute to improving the accuracy of malicious code analysis based on the DLL/API. The main DLL/API information produced through the experiment can be widely used for malicious code variant detection, malicious code group classification, malicious code type classification, and can be used as a common base for various malicious analysis studies.

  Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.


  Cite this article

[IEEE Style]

J. Ha, S. Kim, T. Lee, "Feature Extraction using DLL/API Statistical Analysis and Malware Detection based on Machine Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 4, pp. 730-739, 2018. DOI: 10.7840/kics.2018.43.4.730.

[ACM Style]

Ji-hee Ha, Su-jeong Kim, and Tae-jin Lee. 2018. Feature Extraction using DLL/API Statistical Analysis and Malware Detection based on Machine Learning. The Journal of Korean Institute of Communications and Information Sciences, 43, 4, (2018), 730-739. DOI: 10.7840/kics.2018.43.4.730.

[KICS Style]

Ji-hee Ha, Su-jeong Kim, Tae-jin Lee, "Feature Extraction using DLL/API Statistical Analysis and Malware Detection based on Machine Learning," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 4, pp. 730-739, 4. 2018. (https://doi.org/10.7840/kics.2018.43.4.730)

Search
(IN TITLE, AUTHOR, ABSTRACT,KEYWORDS)

Advanced Search ▼

POPULAR KEYWORDS
(TOP 10 KEYWORDS)

Recent Publications
(LAST 3 YEARS)






pISSN : 1226 - 4717
eISSN : 2287 - 3880