Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares a...Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine（Droid Detector） that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test Droid Detector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. Droid Detector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.展开更多
A method for rapid and simultaneous determination of multiple pyrrolidinium ionic liquid cations by ion chromatography with direct conductivity detection was developed.Chromatographic separations were performed on a c...A method for rapid and simultaneous determination of multiple pyrrolidinium ionic liquid cations by ion chromatography with direct conductivity detection was developed.Chromatographic separations were performed on a cation exchange column using ethylenediamine-acetonitrile as the mobile phase.The effects of chromatographic column and the mobile phase,as well as the column temperature on the retention of the cations were investigated.The retention rules of the cations under different chromatographic conditions were formulated.The retention of the cations followed the carbon number rule.The method has been successfully applied to the determination of three ionic liquids synthesized by a chemical laboratory.展开更多
文摘Smartphones and mobile tablets are rapidly becoming indispensable in daily life. Android has been the most popular mobile operating system since 2012. However, owing to the open nature of Android, countless malwares are hidden in a large number of benign apps in Android markets that seriously threaten Android security. Deep learning is a new area of machine learning research that has gained increasing attention in artificial intelligence. In this study, we propose to associate the features from the static analysis with features from dynamic analysis of Android apps and characterize malware using deep learning techniques. We implement an online deep-learning-based Android malware detection engine（Droid Detector） that can automatically detect whether an app is a malware or not. With thousands of Android apps, we thoroughly test Droid Detector and perform an indepth analysis on the features that deep learning essentially exploits to characterize malware. The results show that deep learning is suitable for characterizing Android malware and especially effective with the availability of more training data. Droid Detector can achieve 96.76% detection accuracy, which outperforms traditional machine learning techniques. An evaluation of ten popular anti-virus softwares demonstrates the urgency of advancing our capabilities in Android malware detection.
基金supported by the Natural Science Foundation of Heilongjiang Province(No.B200909)the Program for Scientific and Technological Innovation Team Construction in Universities of Heilongjiang Province(No.2011TD010)
文摘A method for rapid and simultaneous determination of multiple pyrrolidinium ionic liquid cations by ion chromatography with direct conductivity detection was developed.Chromatographic separations were performed on a cation exchange column using ethylenediamine-acetonitrile as the mobile phase.The effects of chromatographic column and the mobile phase,as well as the column temperature on the retention of the cations were investigated.The retention rules of the cations under different chromatographic conditions were formulated.The retention of the cations followed the carbon number rule.The method has been successfully applied to the determination of three ionic liquids synthesized by a chemical laboratory.
