With the popularity of Android intelligent terminals, malicious applications targeting Android platform are growing rapidly. Therefore, efficient and accurate detection of Android malicious software becomes particularly important. Dynamic API call sequences are widely used in Android malware detection because they can reflect the behaviours of applications accurately. However, the raw dynamic API call sequences are very usually too long to be directly used, and most existing works just use a truncated segment of the sequence or statistical features of the sequence to perform malware detection, which loses the execution order information of applications and consequently results in high false alarm rate. In this work, we propose a method that transforms the dynamic API call sequence into a function call graph, which retains most of the application execution order information with significantly reduced sequence size. To compensate for the missed behaviour information during the transformation, the advanced features of permission requests extracted from the application are utilized. We then propose FGL_Droid, which fusions the transformed function call graph feature and the extracted permission request feature to perform accurate malware detection. Experiments on benchmark dataset show that FGL_Droid achieves a high detection accuracy of 0.975 and a high F-score of 0.978, which are better than the existing methods.

Traditional authentication technologies usually perform identity authentication based on user information verification (e.g., entering the password) or biometric information (e.g., fingerprints). However, there are security risks when applying only these authentication methods. For example, if the password is compromised, it is unlikely to determine whether the user entering the password is legitimate. In this paper, we subdivide biometric information into physiological and behavioral information, and we propose a novel user authentication system, RF-Ubia, which utilizes the low-cost radio frequency identification (RFID) technology to capture unique biological or behavioral information rooted in the user and can be used in two schemes for user authentication.Consisting of an array of nine passive tags and a commercial RFID reader, RF-Ubia provides double assurance for security of identity authentication by combining user information and biometric characteristics. It first verifies the user’s password, and then identifies the biometric characteristics of the legitimate user. Due to the coupling effect among tags, any change in tag signal caused by the user’s touch will affect other tag signals at the same time. Since each user has different fingertip impedance, their touch will cause unique tag signal changes. Therefore, by combining biometric information, the tag array will uniquely identify users. The evaluation results show that RF-Ubia achieves excellent authentication performance with an average recognition rate of 93.8%.

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