Detecting malware evolution using support vector machines-Reference-Cited by-同舟云学术

Detecting malware evolution using support vector machines

Published:2020-04 Issue: Volume:143 Page:113022
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Wadkar Mayuri^ORCID,Di Troia Fabio,Stamp Mark

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference39 articles.

1. Novel feature extraction, selection and fusion for effective malware family classification;Ahmadi,2016

2. Computer viruses and malware;Aycock,2006

3. Bokobza, Y., & Arbiv, Y. (2019). Machine learning for cyber security — Static detection of malicious PE files. https://www.cyberbit.com/blog/endpoint-security/machine-learning-for-cyber-security-static-detection/.

4. Feature ranking using linear SVM;Chang,2008

5. Feature ranking using linear SVM;Chang,2008

Cited by 65 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. A multimodal Windows malware detection method based on hybrid analysis and graph representations;2024 International Conference on Multimedia Analysis and Pattern Recognition (MAPR);2024-08-15

2. An efficient security testing for android application based on behavior and activities using RFE-MLP and ensemble classifier;Multimedia Tools and Applications;2024-06-20

3. Android malware detection and identification frameworks by leveraging the machine and deep learning techniques: A comprehensive review;Telematics and Informatics Reports;2024-06

4. MADESANT: malware detection and severity analysis in industrial environments;Cluster Computing;2024-05-22

5. BenchMFC: A benchmark dataset for trustworthy malware family classification under concept drift;Computers & Security;2024-04