@Article{jqc.2021.017365,
AUTHOR = {Huizhong Sun, Guosheng Xu, Hewei Yu, Minyan Ma, Yanhui Guo, Ruijie Quan},
TITLE = {Malware Detection Based on Multidimensional Time Distribution Features},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {3},
YEAR = {2021},
NUMBER = {2},
PAGES = {55--63},
URL = {http://www.techscience.com/jqc/v3n2/42997},
ISSN = {2579-0145},
ABSTRACT = {Language detection models based on system calls suffer from certain 
false negatives and detection blind spots. Hence, the normal behavior sequences of 
some malware applications for a short period can become malicious behavior 
within a certain time window. To detect such behaviors, we extract a 
multidimensional time distribution feature matrix on the basis of statistical analysis. 
This matrix mainly includes multidimensional time distribution features, 
multidimensional word pair correlation features, and multidimensional word 
frequency distribution features. A multidimensional time distribution model based 
on neural networks is built to detect the overall abnormal behavior within a given 
time window. Experimental evaluation is conducted using the ADFA-LD dataset. 
Accuracy, precision, and recall are used as the measurement indicators of the 
model. An accuracy rate of 95.26% and a recall rate of 96.11% are achieved.},
DOI = {10.32604/jqc.2021.017365}
}