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Unravelling Malware Using Co-Existence Of Features

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Pubblicato in:The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Conference Proceedings vol. 1 (2024)
Autore principale: Omprakash, B
Altri autori: Metan, Jyoti, Konar, Anisha, Patil, Kavitha S, Chiranthan, K K
Pubblicazione:
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Soggetti:
Programming
Information technology
Application programming interface
Machine learning
Social
Accesso online:Citation/Abstract
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Abstract:Conference Title: 2024 Second International Conference on Advances in Information Technology (ICAIT)Conference Start Date: 2024, July 24 Conference End Date: 2024, July 27 Conference Location: Chikkamagaluru, Karnataka, IndiaThis work puts forward machine learning demonstrate depending on co-occurring settled qualities for Mobile adware discovery. Suggested framework suspects Mobile adware asks as aberrant set of occurring permission and application programming interfaces compared to safe apps. For test the premise, we created a novel dataset with occurring permission and application programming interface call on various combination level, specifically the second through fifth levels. The retrieved data of occurring feature in various level were tried on permission alone, application programming interface alone, permission and application programming interface jointly, lastly application programming interface occurrence. For retrieving most pertinent occurring feature, frequently occurring pattern, and association rule method, was utilized. Newly obtained data was derived from Mobile APK sample present in Malgenome Drebin and MalDroid2020 repositories.
DOI:10.1109/ICAIT61638.2024.10690795
Fonte:Science Database