Deep Learning-based Malware Detection
Deep Learning-Based Malware Detection is a computational methodology within the field of malware analysis that employs deep neural networks to automatically identify and classify malicious software by learning hierarchical representations of raw or processed program data, distinguishing itself from traditional detection techniques through its reliance on data-driven feature extraction and complex nonlinear modeling.
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Kexin Pei, Yinzhi Cao, Junfeng Yang et al. · 2017 · 1.2K citations · Full text
Detection of Malicious Code Variants Based on Deep Learning
Zhihua Cui, Fei Xue, Xingjuan Cai et al. · IEEE Transactions on Industrial Informatics · 2018 · 604 citations
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Amrita Vishwa Vidyapeetham
Coimbatore, India
18 h-index
56 publications
4.9K citations
Institute of Information Engineering
Beijing, China
16 h-index
79 publications
3.4K citations
University of Chinese Academy of Sciences
Beijing, China
16 h-index
54 publications
2K citations

Chinese Academy of Sciences
Beijing, China
16 h-index
61 publications
5.7K citations
The University of Texas at San Antonio
San Antonio, United States
16 h-index
18 publications
1.8K citations

Sichuan University
Chengdu, China
14 h-index
45 publications
3.8K citations

Shiraz University
Shiraz, Iran
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30 publications
2.1K citations
Prince Mohammad bin Fahd University
Khobar, Saudi Arabia
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16 publications
1.1K citations
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Repository · 42 publications · 2.4K citations
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