Android malware severely threaten system and user security in terms of privilege escalation, remote control, tariff theft, and privacy leakage. Therefore, it is of great importance and necessity to detect Android malware. In this paper, we present a combination method for Android malware detection based on the machine learning algorithm. First, we construct the control flow graph of the application to obtain API information. Based on the API information, we innovatively construct Boolean, frequency, and time-series data sets. Based on these three data sets, three detection models for Android malware detection regarding API calls, API frequency, and API sequence aspects are constructed. Ultimately, an ensemble model is constructed for conformity. We tested and compared the accuracy and stability of our detection models through a large number of experiments. The experiments were conducted on 10010 benign applications and 10683 malicious applications. The results show that our detection model achieves 98.98% detection precision and has high accuracy and stability. All of the results are consistent with the theoretical analysis in this paper. INDEX TERMS Control flow graph, application programming interface, machine learning, malware detection.

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The telecare medical information system (TMIS) aims to establish telecare services and enable the public to access medical services or medical information at remote sites. Authentication and key agreement is essential to ensure data integrity, confidentiality, and availability for TMIS. Most recently, Chen et al. proposed an efficient and secure dynamic ID-based authentication scheme for TMIS, and claimed that their scheme achieves user anonymity. However, we observe that Chen et al.'s scheme achieves neither anonymity nor untraceability, and is subject to the identity guessing attack and tracking attack. In order to protect user privacy, we propose an enhanced authentication scheme which achieves user anonymity and untraceablity. It is a secure and efficient authentication scheme with user privacy preservation which is practical for TMIS.

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