Android Malware Detection Using Fine-Grained Features

Abstract EN

Nowadays, Android applications declare as many permissions as possible to provide more function for the users, which also poses severe security threat to them.

Although many Android malware detection methods based on permissions have been developed, they are ineffective when malicious applications declare few dangerous permissions or when the dangerous permissions declared by malicious applications are similar with those declared by benign applications.

This limitation is attributed to the use of too few information for classification.

We propose a new method named fine-grained dangerous permission (FDP) method for detecting Android malicious applications, which gathers features that better represent the difference between malicious applications and benign applications.

Among these features, the fine-grained feature of dangerous permissions applied in components is proposed for the first time.

We evaluate 1700 benign applications and 1600 malicious applications and demonstrate that FDP achieves a TP rate of 94.5%.

Furthermore, compared with other related detection approaches, FDP can detect more malware families and only requires 15.205 s to analyze one application on average, which demonstrates its applicability for practical implementation.