Two-path Android Malware Detection Based on N-gram Feature Weighting-Reference-Cited by-同舟云学术

Two-path Android Malware Detection Based on N-gram Feature Weighting

Published:2022-06-23 Issue: Volume: Page:
ISSN:
Container-title:2022 4th International Conference on Information Technology and Computer Communications (ITCC)
language:
Short-container-title:

Author:

Sun Min¹,Zhang Danni¹

Affiliation:

1. College of Computer Science&Technology, Shanxi University, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3548636.3548651

Reference12 articles.

1. Analysis of Android Malicious Applications and Detection Technology [EB/OL]. [2019-05-09]. https://www.secrss.com/articles/10524. Analysis of Android Malicious Applications and Detection Technology [EB/OL]. [2019-05-09]. https://www.secrss.com/articles/10524.

2. Deloitte OPPO. 2021 Mobile Application (APP) Personal Information Protection White Paper [R/OL]. [2021-10]. https://www2.deloitte.com Deloitte OPPO. 2021 Mobile Application (APP) Personal Information Protection White Paper [R/OL]. [2021-10]. https://www2.deloitte.com

3. MMIT. 2021 National Mobile Application Security Observation Report [R/OL].[2022-01]. https://www.freebuf.com/articles/network/320742.html MMIT. 2021 National Mobile Application Security Observation Report [R/OL].[2022-01]. https://www.freebuf.com/articles/network/320742.html

4. Tarar N , Sharma S , Krishna C R . Analysis and Classification of Android Malware using Machine Learning Algorithms[C]// 2018 3rd International Conference on Inventive Computation Technologies (ICICT) . 2018 . Tarar N , Sharma S , Krishna C R . Analysis and Classification of Android Malware using Machine Learning Algorithms[C]// 2018 3rd International Conference on Inventive Computation Technologies (ICICT). 2018.

5. CogramDroid–An approach towards malware detection in Android using opcode ngrams