As network technology has advanced, and as larger and larger quantities of data are being collected, networks are becoming increasingly complex. Various vulnerabilities are being identified in such networks, and related attacks are continuously occurring. To solve these problems and improve the overall quality of network security, a network risk scoring technique using attack graphs and vulnerability information must be used. This technology calculates the degree of risk by collecting information and related vulnerabilities in the nodes and the edges existing in the network-based attack graph, and then determining the degree of risk in a specific network location or the degree of risk occurring when a specific route is passed within the network. However, in most previous research, the risk of the entire route has been calculated and evaluated based on node information, rather than edge information. Since these methods do not include correlations between nodes, it is relatively difficult to evaluate the risk. Therefore, in this paper, we propose a vulnerability Correlation and Attack Graph-based node-edge Scoring System (VCAG-SS) that can accurately measure the risk of a specific route. The proposed method uses the Common Vulnerability Scoring System (CVSS) along with node and edge information. Performing the previously proposed arithmetic evaluation of confidentiality, integrity, and availability (CIA) and analyzing the correlation of vulnerabilities between each node make it possible to calculate the attack priority. In the experiment, the risk scores of nodes and edges and the risk of each attack route were calculated. Moreover, the most threatening attack route was found by comparing the attack route risk. This confirmed that the proposed method calculated the risk of the network attack route and was able to effectively select the network route by providing the network route priority according to the risk score.

The growing dependence on digital systems has heightened the risks posed by cybersecurity threats. This paper proposes a new method for detecting malicious webpages among several adversary activities. As shown in previous studies, malicious URL detection performance is significantly affected by the learning dataset features. The overall performance of different machine learning models varies depending on the data features, and using a particular model alone is not always desirable in any given environment. To address these limitations, we propose an ensemble approach using different machine learning models. Our proposed method outperforms the existing single model by 6%, allowing for the detection of an additional 141 malicious URLs. In this study, repetitive tasks are automated, improving the performance of different machine learning models. In addition, the proposed framework builds an advanced feature set based on URL and web content and includes the most optimized detection model structure. The proposed technology can contribute to define an advanced feature set based on URL and web content and includes the most optimized detection model structure and research on automated technology for the detection of malicious websites, such as phishing websites and malicious code distribution.

With the rapid development of global trade and maritime transportation, a comprehensive emission inventory is necessary to control air pollution from ships as well as to comply with international environmental standards. The purpose of this study is to calculate the volume of emissions from ships in Yeosu and Gwangyang ports in Korea based on a bottom-up approach and supported by the data from the automatic identification system (AIS) and Korean port management information system (Port-MIS). Specifically, a real set of data on specifications of all ships operating at the port in 2019 was collected and an analysis of ship movement using AIS data was conducted by the authors in this study to divide the route into cruising and maneuvering phases as well as to estimate engine’s load factor. Finally, results show that the total amount of air emissions from ships at the port area was 558 thousand tons, which is 96% from CO₂ and 66% from hoteling mode. Additionally, container and tanker ships together emitted the highest volume of air pollution accounting for a share of 73%.

More bridges today require maintenance with age, owing to increasing structural loads from traffic and natural disasters. Routine inspection for damages, including in the aftermath of special events, is conducted by experts. To address the limitations of human inspection, deep-learning-based analysis of bridge damage is being actively conducted. However, such models exhibit deteriorated performance in classifying multiple classes. Most existing algorithms do not use in situ images. Hence, the results of the model training do not accurately reflect the actual damage. This study utilizes an extant method and proposes a new model of combination training by bridge member. By integrating the two approaches, we propose a bridge damaged-object-detection deep-combination framework (BDODC-F). To ensure variety in the type of damaged objects and enhanced model performance, a deep-learning-based super-resolution module is employed. For performance improvement and optimization, a deep-learning combination model based on individual training by bridge member is proposed. The BDODC-F improved the mean average precision by 191.6% and 112.21% in the combination model. We expect the framework to aid engineers in the automated detection and identification of bridge damage.

The quality of road pavements is highly impacted by environmental variables, such as temperature, humidity, and weather; and construction-related variables, such as material quality and time. In this paper, an advanced data collection and analysis system based on big data/cloud was proposed for the use of IoT location-based smart platforms for pavement quality big data at road pavement sites. For the big data platform, a relational database management system (RDBMS) for a general alphanumeric data-based infrastructure for IoT-based systems was designed based on distributed/parallel processing to enable rapid and big data collection and analysis. The structure was established based on a NoSQL-based database to enable real-time high-speed collection and analysis, and the big data platform was developed as a data collection and visualization infrastructure. When the big data system was studied using data analysis methods, the proposed system demonstrated improvements in data collection performance and analysis speed, indicating that analysis results could be derived in real time. Specifically, the data collection processing (create) speed of the NoSQL-based system (0.405 ms) was significantly higher than that of the compared existing system (21.146 ms). Real-time processing capacity was also verified based on quality big data generated on actual road pavements, and the proposed system was proven suitable for the real-time monitoring (the data collection processing) of road pavement quality big data.