Biblio

At present, most Trojan detection methods are based on the features of host and code. Such methods have certain limitations and lag. This paper analyzes the network behavior features and network traffic of several typical Trojans such as Zeus and Weasel, and proposes a Trojan traffic detection algorithm based on machine learning. First, model different machine learning algorithms and use Random Forest algorithm to extract features for Trojan behavior and communication features. Then identify and detect Trojans' traffic. The accuracy is as high as 95.1%. Comparing the detection of different machine learning algorithms, experiments show that our algorithm has higher accuracy, which is helpful and useful for identifying Trojan.

With the popularity of the Internet and mobile intelligent terminals, the number of mobile applications is exploding. Mobile intelligent terminals trend to be the mainstream way of people's work and daily life online in place of PC terminals. Mobile application system brings some security problems inevitably while it provides convenience for people, and becomes a main target of hackers. Therefore, it is imminent to strengthen the security detection of mobile applications. This paper divides mobile application security detection into client security detection and server security detection. We propose a combining static and dynamic security detection method to detect client-side. We provide a method to get network information of server by capturing and analyzing mobile application traffic, and propose a fuzzy testing method based on HTTP protocol to detect server-side security vulnerabilities. Finally, on the basis of this, an automated platform for security detection of mobile application system is developed. Experiments show that the platform can detect the vulnerabilities of mobile application client and server effectively, and realize the automation of mobile application security detection. It can also reduce the cost of mobile security detection and enhance the security of mobile applications.