Most packets arriving in the darknet (or network telescope), which is unused IP address space on the Internet, are related to indiscriminate scanning and attack activities. In recent years, the number of indiscriminate scanning attacks observed on the darknet has increased in diversity and quantity. In our earlier study, we proposed a framework called Dark-TRACER that detects anomalies in spatiotemporal pattern synchronization by using darknet data, with the aim being early detection of malware-caused indiscriminate scanning attacks. Although Dark-TRACER has achieved an average of 126.4 days earlier threat detection, we have not been able to determine whether there is a relationship between the early detections and the actual threats. Hence, in this paper, we perform a cross-checking analysis to identify if any information links the detections and the actual threats. As a result, we confirmed the validity of our early threat detection framework by showing, e.g., that more than 60% of unique hosts overlapped in large-scale threats. In addition, we outline four future studies to address the issue that the present Dark-TRACER has many false-positive alerts. Lastly, the darknet data used in our research has been made publicly available.

Many social phenomena are triggered by public opinion that is formed in the process of opinion exchange among individuals. To date, from the engineering point of view, a large body of work has been devoted to studying how to manipulate individual opinions so as to guide public opinion towards the desired state. Recently, Abebe et al. (KDD 2018) have initiated the study of the impact of interventions at the level of susceptibility rather than the interventions that directly modify individual opinions themselves. For the model, Chan et al. (The Web Conference 2019) designed a local search algorithm to find an optimal solution in polynomial time. However, it can be seen that the solution obtained by solving the above model might not be implemented in real-world scenarios. In fact, as we do not consider the amount of changes of the susceptibility, it would be too costly to change the susceptibility values for agents based on the solution.

Abstract The studies of browsing behavior have gained increasing attention in web analysis for providing better service. Most of the conventional approaches focus on simple indices such as average dwell time and conversion rate. These indices make similar evaluations to websites even if their features are significantly different. Moreover, such statistical indices are not sensitive to the dynamics of users’ interests. In this paper, we propose a new framework for measuring a website’s attractiveness that takes into account both the distribution and dynamics of users’ interests. Within the framework, we define a new index for the website, called Attractiveness Factor, which evaluates the degree of users’ attention. It consists of three procedures: First, we capture the transition of users’ interests during browsing by solving a nonnegative matrix factorization and constrained network flow problems. To accommodate multiple types of interests of a user, we applied a soft clustering as opposed to a hard clustering to model attributes of users and websites. Second, for each website, the feature of each cluster is obtained by fitting the dwell time distribution with Weibull distribution. Finally, we calculate Attractiveness Factor of a website by applying the results of clustering and fitting. Attractiveness Factor depends on the distribution of the dwell time of users interested in the website, which reflects the change of interest of users. Numerical experiments with real web access data of Yahoo Japan News are conducted by solving extremely large-scale optimization problems. They show that Attractiveness Factor captures more exceptional information about browsing behavior more effectively than well-used indices. Attractive factors give low ratings to category pages; however, it can assign high ratings to websites that attract many people, such as hot topic news about the 2018 FIFA World Cup, Japan’s new imperial era’ REIWA,’ and North Korea—the United States Hanoi Summit. Moreover, we demonstrate that Attractiveness Factor can detect the tendency of users’ attention to each website at a given time interval of the day.

One of the most critical problems in bike-sharing services is a bicycle repositioning problem, which is how service providers must relocate their bicycles to maintain the quality of service. In this paper, we propose an end-to-end approach for the bike repositioning problem, which realizes the operator-feasible repositioning plan with cooperation among multiple trucks. Our proposed algorithm consists of three procedures. First, we predict the number of rented and returned bicycles at each station with a deep learning based on the bicycle usage information. Second, we determine the optimal number of bicycles to satisfy the availability of each station by solving an integer optimization problem. Finally, we solve the vehicle routing problem formulated as another integer optimization problem. Based on our algorithm, service operators can actually perform a relocation task based with a reference to the truck capacity, routes, and the number of bicycles to be loaded and unloaded. We demonstrate the applicability of our algorithm in the real world through numerical experiments on the real bicycle data of a Japanese company.

Cyber-Physical Systems (CPSs) are attracting significant attention from a number of industries, including social infrastructure, manufacturing, retail, among others. We can easily gather big datasets of people and transportation movements by utilizing camera and sensor technologies, and create new industrial applications by optimizing and simulating social mobility in the cyberspace. In this paper, we develop the system which automatically performs a series of processes, including object detection, multiple object tracking, and mobility optimization. The mobility of humans and objects is one of the essential components in the real world. Therefore, our system can be widely applied to various application fields. Our major contributions to this paper are remarkable performance improvement of multiple object tracking and building the new mobility optimization engine. In the former, we improve the multiple object tracker using K-Shortest Paths (KSP), which achieves significant data reduction and acceleration by specifying and deleting unnecessary nodes. Numerical experiments show that our proposed tracker is over three times faster than the original KSP tracker while keeping the accuracy. We formulate the mobility optimization problem as the SATisfiability problem (SAT) and the Integer Programming problem (IP) in the latter. Numerical experiments demonstrate that the total transit time can be reduced from 30 s to 10 s. We discuss the characteristics of solutions obtained by the two formulations. We can finally select the appropriate optimization method according to the constraints of calculation time and accuracy for real applications.

In this paper, we propose a practical approach to evacuation planning by utilizing network flow and deep learning algorithms. In recent years, large amounts of data are rapidly being stored in the cloud system, and effective data utilization for solving real-world problems is required more than ever. Hierarchical Data Analysis and Optimization System (HDAOS) enables us to select appropriate algorithms according to the degree of difficulty in solving problems and a given time for the decision-making process, and such selection helps address real-world problems. In the field of emergency evacuation planning, however, the Lexicographically Quickest Flow (LQF) algorithm has an extremely long computation time on a large-scale network, and is therefore not a practical solution. For Osaka city, which is the second-largest city in Japan, we must solve the maximum flow problems on a large-scale network with over 8.3M nodes and 32.8M arcs for obtaining an optimal plan. Consequently, we can feed back nothing to make an evacuation plan. To solve the problem, we utilize the optimal solution as training data of a deep Convolutional Neural Network (CNN). We train a CNN by using the results of the LQF algorithm in normal time, and in emergencies predict the evacuation completion time (ECT) immediately by the well-learned CNN. Our approach provides almost precise ECT, achieving an average regression error of about 2%. We provide several techniques for combining LQF with CNN and addressing numerous movements as CNN's input, which has rarely been considered in previous studies. Hodge decomposition also demonstrates that LQF is efficient from the standpoint of the total distance traveled by all evacuees, which reinforces the validity of the method of utilizing the LQF algorithm for deep learning.