With the rapid growth of Android devices and applications, the Android environment faces more security threats. Malicious applications stealing usersʼ privacy information, sending text messages to trigger deductions, exploiting privilege escalation to control the system, etc., cause significant harm to end users. To detect Android malware, researchers have proposed various techniques, among which the machine learning-based methods with static features of apps as input vectors have apparent advantages in code coverage, operational efficiency, and massive sample detection. In this paper, we investigated Android applicationsʼ structure, analysed various sources of static features, reviewed the machine learning methods for detecting Android malware, studied the advantages and limitations of these methods, and discussed the future directions in this field. Our work will help researchers better understand the current research state, the benefits and weaknesses of each approach, and future technology directions.

With the rapid growth of Android applications, there is an urgent need for powerful Android malware detection technology nowadays. Existing classification models can be summarized with the following two steps-feature extraction and classification model learning. To further enhance the representation ability of existing classification models, this paper presents an Android malicious application detection framework termed Multi-View Information Integration technology (MVIIDroid). To be specific, in our approach, we extract applications' multiple components, transform them into embedding feature vectors and train a Multiple Kernel Learning model as the classifier. To illustrate the effectiveness of our model, we evaluate MVIIDroid on two Android malware datasets of 6820 malware and 6820 benign applications. Results show that we have superior classification performances when separating malware from benign applications. Moreover, we further evaluate MVIIDroid's ability to attribute malicious applications to their actual families. The experimental results well demonstrate the effectiveness of the proposed model.

Biometric authentication is up-and-coming to replace the traditional identity authentication method (e.g., passwords, PIN, identification cards) for its convenience and intelligence. With more and more users using this method, the database becomes more extensive, and the functions are seriously challenged. Data outsourcing has advantages in terms of convenience and cost savings, so it has attracted much research effort. However, due to the biometric's immutability of the whole life, it is extremely sensitive, and disclosing it to a third party is undesirable. In this paper, we address the issue of securely outsourcing biometric database. We propose a novel boolean searchable symmetric encryption (SSE) to construct a secure interactive protocol when outsourcing. A new encrypted database construction method was proposed, using the more efficient boolean vectors. Based on this, We suggest three kinds of expressive SSE, supporting disjunctive query, boolean query, and lightweight settings. We prove the schemes' correctness and security theoretically. Our constructions use simple cryptographic tools, such as symmetric cryptography and pseudo-random functions. They are straightforward to understand and easy to implement. The experiments show that all our schemes are practical and more efficient than the existing methods.

The application of face recognition in classroom teaching has a milestone significance, which opens a new model of teaching process monitoring, provides a new idea for education and teaching reform, and opens a new pattern of the integration of artificial intelligence and education. This paper studies the development trend and research hotspot of face recognition in the field of education through literature metrology and visualized analysis.