Performance comparison of visualization-based malware detection and classification techniques
Author:
Affiliation:
1. College of Computing and Informatics, Universiti Tenaga Nasional,Kajang,Malaysia
2. College of Engineering and IT, Ajman University,Ajman,U.A.E
Funder
Universiti Tenaga Nasional (UNITEN)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10004630/10004620/10004652.pdf?arnumber=10004652
Reference21 articles.
1. A comparison of static, dynamic, and hybrid analysis for malware detection
2. A Survey on Malware Analysis Techniques: Static, Dynamic, Hybrid and Memory Analysis
3. Imaging and evaluating the memory access for malware
4. Detection of malicious software by analyzing the behavioral artifacts using machine learning algorithms
5. Malicious Software Classification Using VGG16 Deep Neural Network’s Bottleneck Features
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. SoK: Visualization-based Malware Detection Techniques;Proceedings of the 19th International Conference on Availability, Reliability and Security;2024-07-30
2. Malred: An Innovative Approach for Detecting Malware Using the Red Channel Analysis of Color Images;2024
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