Android Malware Detection with Classification Based on Hybrid Analysis and N-gram Feature Extraction-Reference-Cited by-同舟云学术

Android Malware Detection with Classification Based on Hybrid Analysis and N-gram Feature Extraction

Published:2022 Issue: Volume: Page:168-184
ISSN:1865-0929
Container-title:Advancements in Smart Computing and Information Security
language:
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Author:

Ravi Eslavath,Kumar Mummadi Upendra

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-23095-0_13

Reference62 articles.

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2. Akter, S., Uddin, M.R., Sajib, S., et al.: Reconceptualizing cybersecurity awareness capability in the data-driven digital economy. Ann. Oper. Res. (2022). https://doi.org/10.1007/s10479-022-04844_8

3. Özer, B.M., Tepecik, A.: Cybersecurity, computer networks phishing, malware, ransomware, and social engineering anti-piracy reviews. In: 2021 3rd International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE (2021)

4. El Yattioui, M.B., El Yattioui, Y.: Saudi Arabia and security in the Middle East: New challenges and opportunities. Handbook of Regional Conflict Resolution Initiatives in the Global South, pp. 43–58 (2022)

5. Jasmeet, K.: Taxonomy of malware: virus, worms and trojan. Int. J. Res. Anal Rev 6.1, 192–196 (2019)

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