Malware Detection and Classification Based on N-Grams Attribute Similarity-Reference-Cited by-同舟云学术

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Malware Detection and Classification Based on N-Grams Attribute Similarity

Published:2017-07 Issue: Volume: Page:
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Container-title:22017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC)
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Author:

Fuyong Zhang,Tiezhu Zhao

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/8004114/8005754/08005908.pdf?arnumber=8005908

Cited by 38 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. NLP-Driven Malware Classification: A Jaccard Similarity Approach;2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems (ICITEICS);2024-06-28

2. CAIMP: Cross-Architecture IoT Malware Detection and Prediction Based On Static Feature;The Computer Journal;2024-05-23

3. EvadeDroid: A practical evasion attack on machine learning for black-box Android malware detection;Computers & Security;2024-04

4. Investigation of The Latest Malware Detection Engines and Lightweight Byte n-Gram Methods with Real Custom Malware;2024 16th International Conference on Computer and Automation Engineering (ICCAE);2024-03-14

5. Fast Preprocessing by Suffix Arrays for Managing Byte n-grams to Detect Malware Subspecies by Machine Learning;Journal of Information Processing;2024

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