DeepMalOb: Deep Detection of Obfuscated Android Malware-Reference-Cited by-同舟云学术

DeepMalOb: Deep Detection of Obfuscated Android Malware

Published:2023 Issue: Volume: Page:307-318
ISSN:1867-8211
Container-title:Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
language:
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Author:

Sawadogo Zakaria^ORCID,Dembele Jean-Marie,Tahar Attoumane,Mendy Gervais^ORCID,Ouya Samuel^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-25271-6_19

Reference25 articles.

1. Ebad, S.A., Darem, A.A., Abawajy, J.H.: Measuring software obfuscation quality-a systematic literature review. IEEE Access 9, 99024–99038 (2021)

2. Zhang, X., Breitinger, F., Luechinger, E., O’Shaughnessy, S.: Android application forensics: a survey of obfuscation, obfuscation detection and deobfuscation techniques and their impact on investigations. Forensic Sci. Int. Digit. Investig. 39, 301285 (2021)

3. Malicious android apps capitalizing on covid-19 promon. https://promon.co/security-news/malicious-android-apps-are-capitalizing-on-covid-19/. Accessed 15 Dec 2020

4. Sawadogo, Z., Mendy, G., Dembelle, J.M., Ouya, S.: Android malware classification: updating features through incremental learning approach (UFILA). In: 2022 24th International Conference on Advanced Communication Technology (ICACT), pp. 544–550. IEEE (2022)

5. Hargreaves, C., Chivers, H.: Recovery of encryption keys from memory using a linear scan. In: 2008 Third International Conference on Availability, Reliability and Security, pp. 1369–1376. IEEE (2008)

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