An in-depth analysis of Android malware using hybrid techniques-Reference-Cited by-同舟云学术

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An in-depth analysis of Android malware using hybrid techniques

Published:2018-03 Issue: Volume:24 Page:25-33
ISSN:1742-2876
Container-title:Digital Investigation
language:en
Short-container-title:Digital Investigation

Author:

Kabakus Abdullah Talha^ORCID,Dogru Ibrahim Alper

Publisher

Elsevier BV

Subject

Law,Medical Laboratory Technology,Computer Science Applications

Reference63 articles.

1. Malware Analysis and Privacy Policy Enforcement Techniques for Android Applications;Ali-Gombe,2017

2. Improving dynamic analysis of android apps using hybrid test input generation;Alzaylaee,2017

3. Google;Android – Google Play Protect,2017

4. Effective and Explainable Detection of Android Malware in Your Pocket;Arp,2014

5. Targeted and depth-first exploration for systematic testing of android apps;Azim;ACM SIGPLAN Not.,2013

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1. A Scalable Vertical Federated Learning Framework for Analytics in the Cybersecurity Domain;2024 32nd Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP);2024-03-20

2. Machine Learning for Android Malware Detection: Mission Accomplished? A Comprehensive Review of Open Challenges and Future Perspectives;Computers & Security;2024-03

3. IMCNN:Intelligent Malware Classification using Deep Convolution Neural Networks as Transfer learning and ensemble learning in honeypot enabled organizational network;Computer Communications;2024-02

4. Hybrid machine learning model for malware analysis in android apps;Pervasive and Mobile Computing;2024-01

5. A Systematic Review and Future Perspective of Android Malware Detection Based Machine Learning Techniques;2023 IEEE International Conference on ICT in Business Industry & Government (ICTBIG);2023-12-08

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