A state-of-the-art survey of malware detection approaches using data mining techniques
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Title
A state-of-the-art survey of malware detection approaches using data mining techniques
Authors
Keywords
Data mining, Malware detection, Classification, Behavior-based, Signature-based
Journal
Human-centric Computing and Information Sciences
Volume 8, Issue 1, Pages -
Publisher
Springer Nature
Online
2018-01-15
DOI
10.1186/s13673-018-0125-x
References
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- (2016) Yujie Fan et al. EXPERT SYSTEMS WITH APPLICATIONS
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- (2016) Altyeb Altaher NEURAL COMPUTING & APPLICATIONS
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- (2016) Alejandro Martín et al. SOFT COMPUTING
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- Droiddetector: android malware characterization and detection using deep learning
- (2016) Zhenlong Yuan et al. TSINGHUA SCIENCE AND TECHNOLOGY
- AMAL: High-fidelity, behavior-based automated malware analysis and classification
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- Malware behavioural detection and vaccine development by using a support vector model classifier
- (2015) Ping Wang et al. JOURNAL OF COMPUTER AND SYSTEM SCIENCES
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- Opcode sequences as representation of executables for data-mining-based unknown malware detection
- (2011) Igor Santos et al. INFORMATION SCIENCES
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