North Korea continues to upgrade and display its long-range rocket launchers to emphasize its military strength. Recently Republic of Korea kicked off the development of anti-artillery interception system similar to Israel’s “Iron Dome”, designed to protect against North Korea’s arsenal of long-range rockets. The system may not work smoothly without the function assigning interceptors to incoming various-caliber artillery rockets. We view the assignment task as a dynamic weapon target assignment (DWTA) problem. DWTA is a multistage decision process in which decision in a stage affects decision processes and its results in the subsequent stages. We represent the DWTA problem as a Markov decision process (MDP). Distance from Seoul to North Korea’s multiple rocket launchers positioned near the border, limits the processing time of the model solver within only a few second. It is impossible to compute the exact optimal solution within the allowed time interval due to the curse of dimensionality inherently in MDP model of practical DWTA problem. We apply two reinforcement-based algorithms to get the approximate solution of the MDP model within the time limit. To check the quality of the approximate solution, we adopt Shoot-Shoot-Look(SSL) policy as a baseline. Simulation results showed that both algorithms provide better solution than the solution from the baseline strategy.

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation. We first generate gridded trajectory images by mapping the raw vessel trajectories into two dimensional matrix. Based on the gridded image input, we test the proposed model along with the other deep autoencoder-based models for the abnormal trajectory data generated through rotation and speed variation from normal trajectories. We show that the proposed model improves detection accuracy for the generated abnormal trajectories compared to the other models.

Maritime monitoring requirements have been beyond human operators capabilities due to the broadness of the coverage area and the variety of monitoring activities, e.g. illegal migration, or security threats by foreign warships. Abnormal vessel movement can be defined as an unreasonable movement deviation from the usual trajectory, speed, or other traffic parameters. Detection of the abnormal vessel movement requires the operators not only to pay short-term attention but also to have long-term trajectory trace ability. Recent advances in deep learning have shown the potential of deep learning techniques to discover hidden and more complex relations that often lie in low dimensional latent spaces. In this paper, we propose a deep autoencoder-based clustering model for automatic detection of vessel movement anomaly to assist monitoring operators to take actions on the vessel for more investigation.

IT security service provides customers with the capability of protecting the networked information asset and infrastructures, and the scope of security service is expanding from a technology-intensive task to a comprehensive protection system for IT environment. To improve the quality of this service, a research model which help assess the quality is required. Several research models have been proposed and used in various service areas, but few cases are found for IT security service. In this work, a research model for the IT security quality has been proposed, based on research models such as SERVQUAL and E-S-QUAL. With the proposed model, factors which affect the service quality and the best quality measure have been identified. And the feasibility of using quantitative measures for quality has been examined. For analysis, structural equation modeling and various statistical methods such as principal component analysis were used. The result shows that satisfaction is the most significant measure affected by the proposed quality factors. Two quality factors, fulfillment and empathy, are the main determinants of the service quality. This leads to a strategy of quality improvement based on factors of emotion and perception, not of technology. The quantitative measures are considered as promising alternative measures, when combined with other measures. In order to design reliable quantitative measures, more work should be done on target processing time and users’ expectation. It is hoped that work of this research will provide efficient tools and methods to improve the quality of IT security service and help future research works for other IT service areas.

IT security service protects their networked information asset and infrastructures using experts’ knowledge. To improve its service quality, by exploring quality factors and assessing the quality level, an approach based on a research model is required. Several research models have been proposed and used in various service area, but few cases of IT security service are found. In this work, a research model of service quality has been proposed, based on the existing research models such as SERVQUAL and E-S-QUAL. The factors which affect the quality of IT security service and the best quality measure including quantitative measures have been identified. By comparing them, the feasibility of using quantitative measures for quality measures has been addressed. For analysis, structural equation modeling and various statistical methods were used. The result shows that, among the three quality measures, satisfaction is the most significantly affected one by quality factors. The quantitative measures show positive correlation with other perceived quality measures. The analysis of the result provides several suggestions which can be used in measuring and improving the quality of IT security service. Emotional factors such as empathy are as important as the swiftness of service, which leads to a strategy of quality improvement based on customer satisfaction, not solely on technology only. And, in order to design reliable quantitative measures, standards such as rational target processing time should be first established. The work of this research will provide efficient tools to improve the quality of IT security service and help the quality research for other service areas.

The Hausdorff distance is commonly used as a similarity measure between two-dimensional binary images. Since the document images may be contaminated by a variety of noise sources during transmission, scanning or conversion to digital form, the measure should be robust to the noise. Original Hausdorff distance has been known to be sensitive to outliers. Transforming the given image to grayscale image is one of methods to deal with the noises. In this paper, we propose a Hausdorff distance applied to grayscale images. The proposed method is tested with synthetic images with various levels of noises and compared with other methods to show its robustness.