Towards using unstructured user input request for malware detection-Reference-Cited by-同舟云学术

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Towards using unstructured user input request for malware detection

Published:2020-06 Issue: Volume:93 Page:101783
ISSN:0167-4048
Container-title:Computers & Security
language:en
Short-container-title:Computers & Security

Author:

Olukoya Oluwafemi^ORCID,Mackenzie Lewis,Omoronyia Inah

Publisher

Elsevier BV

Subject

Law,General Computer Science

Reference97 articles.

1. Droidapiminer: mining api-level features for robust malware detection in android;Aafer,2013

2. Markets for cybercrime Tools and Stolen data: Hackers’ Bazaar;Ablon,2014

3. Privacy and human behavior in the age of information;Acquisti;Science,2015

4. Identifying android malware using dynamically obtained features;Afonso;J. Comput. Virol. Hacking Tech.,2015

5. Androzoo: Collecting millions of android apps for the research community;Allix,2016

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1. Leveraging Machine Learning for Android Malware Analysis: Insights from Static and Dynamic Techniques;Engineering, Technology & Applied Science Research;2024-08-02

2. Maloid-DS: Labeled Dataset for Android Malware Forensics;IEEE Access;2024

3. Nation-State Threat Actor Attribution Using Fuzzy Hashing;IEEE Access;2023

4. Why GloVe Shows Negative Effects in Malware Classification;2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2);2022-11-11

5. An Android Malware Detection Leveraging Machine Learning;Wireless Communications and Mobile Computing;2022-05-06

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