Zero-Vuln: Using deep learning and zero-shot learning techniques to detect zero-day Android malware-Reference-Cited by-同舟云学术

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Zero-Vuln: Using deep learning and zero-shot learning techniques to detect zero-day Android malware

Published:2023-07-19 Issue: Volume: Page:
ISSN:
Container-title:2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)
language:
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Author:

Sawadogo Zakaria¹,Dembele Jean-Marie²,Mendy Gervais³,Ouya Samuel³

Affiliation:

1. Gaston Berger University,Laboratory of Numerical Analysis and Computer Science,Dakar,Senegal

2. Gaston Berger University,Lab of Numerical Analysis and Computer Science,Saint Louis,Senegal

3. Cheikh Anta Diop University,Computer Science, Telecommunications and Applications,Dakar,Senegal

Funder

Innovation Fund

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10252163/10252090/10252803.pdf?arnumber=10252803

Reference19 articles.

1. Multi-view deep learning for zero-day Android malware detection

2. Didroid: Android malware classification and characterization using deep image learning;rahali;2020 the 10th International Conference on Communication and Network Security ICCNS 2020,2021

3. Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation;powers;CoRR,2020

4. Zero-Day Malware Detection and Effective Malware Analysis Using Shapley Ensemble Boosting and Bagging Approach

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