The security evaluation for an information network system is an important management tool to insure its normal operation. We must realize the significance of the comprehensive network security risks. A network evaluat...The security evaluation for an information network system is an important management tool to insure its normal operation. We must realize the significance of the comprehensive network security risks. A network evaluation model and the algorithm are presented and adapt the hierarchical method to characterize the security risk situation. The evaluation method is used to evaluate the key nodes and the mathematics is used to analyze the whole network security situation. Compared with others, the method can automatically create a rule-based security evaluation model to evaluate the security threat from the individual security elements and the combination of security elements, and then evaluation the network situation. It is shown that this system provides a valuable model and algorithms to help to find the security rules, adjust the security展开更多
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
文摘The security evaluation for an information network system is an important management tool to insure its normal operation. We must realize the significance of the comprehensive network security risks. A network evaluation model and the algorithm are presented and adapt the hierarchical method to characterize the security risk situation. The evaluation method is used to evaluate the key nodes and the mathematics is used to analyze the whole network security situation. Compared with others, the method can automatically create a rule-based security evaluation model to evaluate the security threat from the individual security elements and the combination of security elements, and then evaluation the network situation. It is shown that this system provides a valuable model and algorithms to help to find the security rules, adjust the security
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.
