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Abstract：
With the development of the Internet, malicious code attacks have increased exponentially, with malicious code variants ranking as a key threat to Internet security. The ability to detect variants of malicious code is critical for protection against security breaches, data theft, and other dangers. Current methods for recognizing malicious code have demonstrated poor detection accuracy and low detection speeds. This paper proposed a novel method that used deep learning to improve the detection of malware variants. In prior research, deep learning demonstrated excellent performance in image recognition. To implement our proposed detection method, we converted the malicious code into grayscale images. Then, the images were identified and classified using a convolutional neural network (CNN) that could extract the features of the malware images automatically. In addition, we utilized a bat algorithm to address the data imbalance among different malware families. To test our approach, we conducted a series of experiments on malware image data from Vision Research Lab. The experimental results demonstrated that our model achieved good accuracy and speed as compared with other malware detection models.
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Source ：
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
ISSN： 1551-3203
Year： 2018
Issue： 7
Volume： 14
Page： 3187-3196
1 2 . 3 0 0
JCR@2022
ESI Discipline： ENGINEERING;
ESI HC Threshold：156
JCR Journal Grade：1
Cited Count：
WoS CC Cited Count： 464
SCOPUS Cited Count： 571
ESI Highly Cited Papers on the List： 39 Unfold All
WanFang Cited Count：
Chinese Cited Count：
30 Days PV： 6
Affiliated Colleges：
