Stock market volatility is influenced by information release, dissemination, and public acceptance. With the increasing volume and speed of social media, the effects of Web information on stock markets are becoming increasingly salient. However, studies of the effects of Web media on stock markets lack both depth and breadth due to the challenges in automatically acquiring and analyzing massive amounts of relevant information. In this study, we systematically reviewed 229 research articles on quantifying the interplay between Web media and stock markets from the fields of Finance, Management Information Systems, and Computer Science. In particular, we first categorized the representative works in terms of media type and then summarized the core techniques for converting textual information into machine-friendly forms. Finally, we compared the analysis models used to capture the hidden relationships between Web media and stock movements. Our goal is to clarify current cutting-edge research and its possible future directions to fully understand the mechanisms of Web information percolation and its impact on stock markets from the perspectives of investors cognitive behaviors, corporate governance, and stock market regulation.

Position information is an important aspect of a mobile device’s context. While GPS is widely used to provide location information, it does not work well indoors. Wi-Fi network infrastructure is found in many public facilities and can be used for indoor positioning. In addition, the ubiquity of Wi-Fi-capable devices makes this approach especially cost-effective. In recent years, “folksonomy”-like systems such as Wikipedia or Delicious Social Bookmarking have achieved huge successes. User collaboration is the defining characteristic of such systems. For indoor positioning mechanisms, it is also possible to incorporate collaboration in order to improve system performance, especially for fingerprinting-based approaches. In this article, a robust and efficient model is devised for integrating human-centric collaborative feedback within a baseline Wi-Fi fingerprinting-based indoor positioning system. Experiments show that the baseline system performance (i.e., positioning error and precision) is improved by collecting both positive and negative feedback from users. Moreover, the feedback model is robust with respect to malicious feedback, quickly self-correcting based on subsequent helpful feedback from users.

With the rapid growth of data exfiltration carried out by cyber attacks, Covert Timing Channels (CTC) have become an imminent network security risk that continues to grow in both sophistication and utilization. These types of channels utilize inter-arrival times to steal sensitive data from the targeted networks. CTC detection relies increasingly on machine learning techniques, which utilize statistical-based metrics to separate malicious (covert) traffic flows from the legitimate (overt) ones. However, given the efforts of cyber attacks to evade detection and the growing column of CTC, covert channels detection needs to improve in both performance and precision to detect and prevent CTCs and mitigate the reduction of the quality of service caused by the detection process. In this article, we present an innovative image-based solution for fully automated CTC detection and localization. Our approach is based on the observation that the covert channels generate traffic that can be converted to colored images. Leveraging this observation, our solution is designed to automatically detect and locate the malicious part (i.e., set of packets) within a traffic flow. By locating the covert parts within traffic flows, our approach reduces the drop of the quality of service caused by blocking the entire traffic flows in which covert channels are detected. We first convert traffic flows into colored images, and then we extract image-based features for detection covert traffic. We train a classifier using these features on a large data set of covert and overt traffic. This approach demonstrates a remarkable performance achieving a detection accuracy of 95.83% for cautious CTCs and a covert traffic accuracy of 97.83% for 8 bit covert messages, which is way beyond what the popular statistical-based solutions can achieve.

Covert timing channels are an important alternative for transmitting information in the world of the Internet of Things (IoT). In covert timing channels data are encoded in inter-arrival times between consecutive packets based on modifying the transmission time of legitimate traffic. Typically, the modification of time takes place by delaying the transmitted packets on the sender side. A key aspect in covert timing channels is to find the threshold of packet delay that can accurately distinguish covert traffic from legitimate traffic. Based on that we can assess the level of dangerous of security threats or the quality of transferred sensitive information secretly. In this paper, we study the inter-arrival time behavior of covert timing channels in two different network configurations based on statistical metrics, in addition we investigate the packet delaying threshold value. Our experiments show that the threshold is approximately equal to or greater than double the mean of legitimate inter-arrival times. In this case covert timing channels become detectable as strong anomalies.

Unlike the flat-view matrix, tensor provides an elegant representation to preserve the structure of data in the multidimensional nature. The wide use of tensor-based approaches highlights the limitation of matrix-based methods and has generated increasing needs in multidimensional data analytics. Driven by such demands, tensor decomposition with automatic rank determination has recently emerged as a promising tool for image processing, wireless communications and neural network compression. However, unlike the success in matrix-based schemes of exploring the correlation for performance improvement, existing tensor-based methods fail to consider the correlation structure in the tensor. In this paper, we propose to exploit the tensor intra-dimension correlation within the framework of sparse Bayesian learning. We provide the sparsity-inducing probabilistic models under both canonical polyadic decomposition and Tucker decomposition to capture the correlation structure effectively. We derive an efficient model inference method with a fast convergence rate based on the expectation-maximization algorithm. Furthermore, a complexity reduction method is proposed to speed up the computation by exploiting several properties of the Kronecker product. We further extend the proposed framework for the incomplete tensor corrupted by sparse outliers to achieve effective tensor completion and outlier mitigation. Simulation results demonstrate the superior performance of the proposed framework over those ignoring the intra-dimension correlation in terms of accuracy and sparsity.