Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders

Jin Young Kim; Seok-Jun Bu; Sung‐Bae Cho

doi:10.1016/j.ins.2018.04.092

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JOURNAL ARTICLE

Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders

Jin Young Kim Seok-Jun Bu Sung‐Bae Cho

Year: 2018 Journal: Information Sciences Vol: 460-461 Pages: 83-102 Publisher: Elsevier BV

DOI: 10.1016/j.ins.2018.04.092

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Keywords:

Malware Computer science Discriminator Artificial intelligence Autoencoder Deep learning Machine learning Convolutional neural network Generator (circuit theory) Stability (learning theory) Generative adversarial network Data mining Pattern recognition (psychology) Detector Computer security

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250

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20.23

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1.00

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Topics

Advanced Malware Detection Techniques

Physical Sciences → Computer Science → Signal Processing

Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Digital Media Forensic Detection

Physical Sciences → Computer Science → Computer Vision and Pattern Recognition

Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders

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Citation History

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