Invention Grant
- Patent Title: Mobile application malicious behavior pattern detection method based on API call graph extraction and recording medium and device for performing the same
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Application No.: US17289172Application Date: 2020-11-26
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Publication No.: US11768938B2Publication Date: 2023-09-26
- Inventor: Jeong Hyun Yi , Jin Sung Kim
- Applicant: Foundation of Soongsil University-Industry Cooperation
- Applicant Address: KR Seoul
- Assignee: FOUNDATION OF SOONGSIL UNIVERSITY-INDUSTRY COOPERATION
- Current Assignee: FOUNDATION OF SOONGSIL UNIVERSITY-INDUSTRY COOPERATION
- Current Assignee Address: KR Seoul
- Agency: Stein IP, LLC
- Priority: KR 20200157078 2020.11.20
- International Application: PCT/KR2020/016913 2020.11.26
- International Announcement: WO2022/107963A 2022.05.27
- Date entered country: 2021-04-27
- Main IPC: G06F21/56
- IPC: G06F21/56 ; G06F21/52

Abstract:
A mobile application malicious behavior pattern detection method based on Application Programming Interface (API) call graph extraction includes extracting an API Call Graph (ACG) representing an API call flow from benign applications and applications which perform malicious behavior, generating and vectorizing a training dataset for deep learning using the extracted ACG, generating a deep learning algorithm prediction model by training with the vectorized training dataset, extracting ACG features used in the malicious behavior from the generated prediction model and extracting a malicious behavior pattern from an intersection of the malicious applications, and classifying an application which performs malicious behavior through similarity comparison between the extracted malicious behavior pattern and a pattern extracted from the target application. Accordingly, it is possible to detect the malicious behavior itself using the ACG representing an API call flow.
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