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  • 标题:Concordance and Term Frequency in Analyzing API Calls for Malware Behavior Detection
  • 本地全文:下载
  • 作者:Mina Martini ; Patrick Udoudo Unyime ; Ette Harrison Etuk
  • 期刊名称:Journal of Computer Science
  • 印刷版ISSN:1549-3636
  • 出版年度:2019
  • 卷号:15
  • 期号:9
  • 页码:1307-1319
  • DOI:10.3844/jcssp.2019.1307.1319
  • 出版社:Science Publications
  • 摘要:Application Programming Interface (API) is used for the software to interact with an operating system to do certain task such as opening file, deleting file and many more. Programmers use this API to make it easier for their program to communicate with the operating system without having the knowledge of the hardware of the target system. Malware author is an attacker that may belong to an organization or work for themselves. Some malware author has the capabilities to write their own malware, uses the same kind of APIs that is used to create normal programs to create malware. There are many researches done in this field, however, most researchers used n-gram to detect the sequence of API calls and although it gave good results, it is time consuming to process through all the output. This is the reason why this paper proposed to use Concordance to search for the API call sequence of a malware because it uses KWIC (Key Word in Context), thus only displayed the output based on the queried.
  • 关键词:Concordance; KWIC; API Call Sequence; Malware Behaviors; Dynamic Analysis
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