Biblio

The prevalence of mobile devices (smartphones) along with the availability of high-speed internet access world-wide resulted in a wide variety of mobile applications that carry a large amount of confidential information. Although popular mobile operating systems such as iOS and Android constantly increase their defenses methods, data shows that the number of intrusions and attacks using mobile applications is rising continuously. Experts use techniques to detect malware before the malicious application gets installed, during the runtime or by the network traffic analysis. In this paper, we first present the information about different categories of mobile malware and threats; then, we classify the recent research methods on mobile malware traffic detection.

The incidence of abnormal road traffic events, especially abnormal traffic congestion, is becoming more and more prominent in daily traffic management in China. It has become the main research work of urban traffic management to detect and identify traffic congestion incidents in time. Efficient and accurate detection of traffic congestion incidents can provide a good strategy for traffic management. At present, the detection and recognition of traffic congestion events mainly rely on the integration of road traffic flow data and the passing data collected by electronic police or devices of checkpoint, and then estimating and forecasting road conditions through the method of big data analysis; Such methods often have some disadvantages such as low time-effect, low precision and small prediction range. Therefore, with the help of the current large and medium cities in the public security, traffic police have built video surveillance equipment, through computer vision technology to analyze the traffic flow from video monitoring, in this paper, the motion state and the changing trend of vehicle flow are obtained by using the technology of vehicle detection from video and multi-target tracking based on deep learning, so as to realize the perception and recognition of traffic congestion. The method achieves the recognition accuracy of less than 60 seconds in real-time, more than 80% in detection rate of congestion event and more than 82.5% in accuracy of detection. At the same time, it breaks through the restriction of traditional big data prediction, such as traffic flow data, truck pass data and GPS floating car data, and enlarges the scene and scope of detection.