A Comparison of Machine and Deep Learning Models for Detection and Classification of Android Malware Traffic-Reference-Cited by-同舟云学术

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A Comparison of Machine and Deep Learning Models for Detection and Classification of Android Malware Traffic

Published:2022-06-30 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Symposium on Computers and Communications (ISCC)
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Author:

Bovenzi Giampaolo¹,Cerasuolo Francesco¹,Montieri Antonio¹,Nascita Alfredo¹,Persico Valerio¹,Pescape Antonio¹

Affiliation:

1. University of Napoli “Federico II”,DIETI,Napoli,Italy

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9912737/9912463/09912986.pdf?arnumber=9912986

Cited by 11 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Enhancing Flow Embedding Through Trace: A Novel Self-supervised Approach for Encrypted Traffic Classification;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Malware Classification Using Machine Learning Models;Procedia Computer Science;2024

3. A Semi-supervised Learning Method for Malware Traffic Classification with Raw Bitmaps;Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering;2024

4. Detection of Android Malware in the Internet of Things through the K-Nearest Neighbor Algorithm;Sensors;2023-08-18

5. A Deep-Vision-Based Multi-class Classification System of Android Malware Apps;Security Informatics and Law Enforcement;2023-07-03

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