Malware Detection with Obfuscation Techniques on Android Using Dynamic Analysis
Author:
Affiliation:
1. Sampoema University,Faculty of Engineering and Technology,Jakarta,Indonesia
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10009906/10009914/10010359.pdf?arnumber=10010359
Reference19 articles.
1. Visualizing the outcome of dynamic analysis of Android malware with VizMal
2. Malware Detection and Classification Model Using Machine Learning Random Forest Approach
3. A Bayesian probability model for Android malware detection;mat;ICT Express,2021
4. Machine Learning Classifiers for Android Malware Detection
5. Android Malware Detection Using Machine Learning
Cited by 2 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. Dynamic Analysis of a Malware Sample: Recognizing its Behavior using Forensic Application;2023 4th IEEE Global Conference for Advancement in Technology (GCAT);2023-10-06
2. Smartphone Malware Detection Based on Enhanced Correlation-Based Feature Selection on Permissions;Proceedings of Data Analytics and Management;2023
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