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Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Piezoelectrics & Acoustooptics is the property of Piezoelectric & Acoustooptic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Based on the whole process of malware detection in the Android system. this paper compared and analyzed the status and progress of the research at home and abroad, introduced the source and function of the standardized data samples from the point of view of sample acquisition, and expounded the principles to be followed in the feature selection. This paper compared and analyzed the advantages and disadvantages of various detection methods from the point of view of detection methods, and summed up and summarized the selection methods of feature data sets and the evaluation methods of experimental results. Finally, it prospected the research and development direction of the future Android malware detection method. [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the openness of Android system, malware poses a threat to users of Android devices by implementing various malicious behaviors. At present, most of the existing researches focus on coarse-grained malicious detection, that is, whether an Android application is malicious or not. Aiming at this problem, this paper proposed a fine-grained malicious behavior classification method based on static behavior features. This method extracted and optimized multi-dimensional behavior characteristics, including API calls, permissions, intents and package dependencies. And then used random forest to classify malicious behaviors. It conducted the experiments on 24 553 malicious Android application samples in 73 malware families from multiple application markets. The experimental results show that the accuracy of fine-grained malware classification reached to 95. 88%, which is better than other comparison methods. [ABSTRACT FROM AUTHOR]

Many Android applications have more functions than their types, and they need to acquire more permissions. Excessive permissions may bring some security risks. To address these issues, this paper proposed an Android malware detection method based on meta information. First, it extracted the LDA theme through the description of Android application, implemented the data dimensionality reduction, and grouped applications by the functional type used the K-means clustering al gorithm. Then, for all applications belonging to the same functional type, it extracted their permission information, and took the permission features as the research object, used KNN algorithm to classify and detect the malicious software of Android. The experimental results obtain the average accuracy of 94. 81 % and prove the validity and high accuracy of the method. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Shenzhen University Humanities & Social Sciences is the property of Journal of Shenzhen University (Humanities & Social Sciences) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to the shortage of traditional door assessment system, such as single function, poor reliability and low security, a multi-door access control system based on fingerprint identification technology is put forward in this paper. HT66FU50 is selected as the control core of this system, it mainly adopts fingerprint recognition technology to control two or more doors. When the system gets illegal fingerprints for three times, it will send a message and ring a call to the telephone of the owner through the GSM. Meanwhile it connects the camera by Wi-Fi so that it can capture image and video. PC and Android phone can also monitor and control the situation through Wi-Fi technology, so that the access control system is safer and more reliable. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)