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An Android Malware Detection Approach Based on Summation of Multi-order Derivatives LSTM
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Abstract
With the popularity of the Android operating system on mobile devices, unscrupulous people prefer to attack Android devices, which results in the emergence of Android malware. This paper proposes an Android malware detection approach based on Summation of Multi-order Derivatives LSTM (SoMD-LSTM). In order to learn various runtime features of Android malware, the summation of multi-order derivatives is introduced to LSTM. As a result, SoMD-LSTM can describe the dynamic behavior of Android malware quantitatively by the summation of multi-order derivatives. Experiments show that the proposed Android malware detection approach has a better performance than LSTM and other methods.
Title: An Android Malware Detection Approach Based on Summation of Multi-order Derivatives LSTM
Description:
Abstract
With the popularity of the Android operating system on mobile devices, unscrupulous people prefer to attack Android devices, which results in the emergence of Android malware.
This paper proposes an Android malware detection approach based on Summation of Multi-order Derivatives LSTM (SoMD-LSTM).
In order to learn various runtime features of Android malware, the summation of multi-order derivatives is introduced to LSTM.
As a result, SoMD-LSTM can describe the dynamic behavior of Android malware quantitatively by the summation of multi-order derivatives.
Experiments show that the proposed Android malware detection approach has a better performance than LSTM and other methods.
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