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 be...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.展开更多
基金supported by the National Key Research and Development Program of China(No.2017YFB0801900).
文摘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.
