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The intelligent traffic signal (I-SIG) system aims to perform automatic and optimal signal control based on traffic situation awareness by leveraging connected vehicle (CV) technology. However, the current signal control algorithm is highly vulnerable to CV data spoofing attacks. These vulnerabilities can be exploited to create congestion in an intersection and even trigger a cascade failure in the traffic network. To avoid this issue, timely and accurate congestion attack detection and identification are essential. This work proposes a congestion attack detection approach by combining empirical prediction and analytical verification. First, we collect a range of traffic images that correspond to specific traffic snapshots which are vulnerable to potential data spoofing attacks. Based on these traffic images, an improved generative adversarial network is trained to predict whether a forthcoming attack will cause congestion with a high probability. Meanwhile, we define a group of traffic flow features. After exploring features and conducting a thorough analysis, a TGRU (tree-regularized gated recurrent unit)-based approach is proposed to verify whether congestion occurs. When we find a possible attack that can cause congestion with high probability and subsequent traffic flows also prove congestion, we can say there is a congestion attack. Thus, we can realize timely and accurate congestion attack detection by integrating empirical prediction and analytical verification. Extensive experiments demonstrate that our approach performs well in congestion attack detection accuracy and timeliness.

With the development of the Internet, user comments produced an unprecedented impact on information acquisition, goods purchase, and other aspects. For example, the user comments can quickly render a topic the focus of discussion in social networks. It can promote the sales of goods in e-commerce, and it influences the ratings of books, movies, or albums. Among these network applications and services, “astroturfing,” a kind of online suspicious behavior, can generate abnormal, damaging, and even illegal behaviors in cyberspace that mislead public perception and bring a bad effect on Internet users and society. Hence, the manner of detecting and combating astroturfing behavior has become highly urgent, attracting interest from researchers both from information technology and sociology. In the current paper, we restudy it mainly from the perspective of information technology, summarize the latest research findings of astroturfing detection, analyze the astroturfing feature, classify the machine learning-based detection methods and evaluation criteria, and introduce the main applications. Different from the previous surveys, we also discuss the new future directions of astroturfing detection, such as cross-domain astroturfing detection and user privacy protection.

Dioscorea zingiberensis accumulates abundant steroidal saponins, such as dioscin, which is the principal bioactive ingredient displaying a wide range of pharmacological activities. Diosgenin is the aglycone of dioscin, and recently, genes encoding cytochrome P450 enzymes in the late steps of diosgenin biosynthesis have been isolated. Diosgenin was successfully synthesized in the cholesterol-producing yeasts. From diosgenin to dioscin, one glucose and two rhamnose groups need to be added. Although genes encoding UDP-glucosyltransferases converting diosgenin to trillin were isolated, genes encoding UDP-rhamnosyltransferases involved in dioscin biosynthesis remain unknown. In this study, we isolated the cDNA encoding the trillin rhamnosyltransferase (designated DzGT1) from D. zingiberensis. Heterologous expression of DzGT1 in Escherichia coli cells showed that the gene product exhibits an enzyme activity that glycosylates the trillin to form prosapogenin A of dioscin (PSA). The transcript level of DzGT1 is in accord with PSA accumulation in different organs of D. zingiberensis. Integration of the biochemical, metabolic, and transcriptional data supported the function of DzGT1 in dioscin biosynthesis. The identification and characterization of DzGT1 will help understand the metabolism of steroidal saponins in D. zingiberensis and provide candidate UDP-rhamnosyltransferase for efficient production of PSA, dioscin, and relevant steroidal saponins in microbial hosts.