Search

With the recent proliferation of social media, the spread of disinformation and misinformation has become a global risk. This study analyzes and characterizes the relationship between human emotions and other elements (social bots and echo chambers), which are major factors in the spread of information, including disinformation and misinformation. Specifically, we obtain from X (formerly Twitter) the case that was fact-checked as disinformation in Japan and analyze the relationship between each factor in a time sequence using existing tools and methods. After confirming the similar results to previous studies that emotional posts were more likely to be spread, our analysis showed the possibility that social bots were highly influential in amplifying negative sentiment in the early stages of spread. Regarding emotions and echo chambers, we observed that the increase in negative emotions and the formation of echo chambers followed a similar time sequence. In addition, we confirmed that the amplification of negative emotions precedes the formation of echo chambers. The results of this analysis suggested the importance of detecting social bot activity in the early stages of information diffusion and controlling the amplification of negative emotions.

The proliferation of disinformation has become an issue in recent years due to the widespread use of social media. This study analyzes the recent activities of social bots in Japan, which may be used for the purpose of spreading disinformation, and clarifies the characteristics of influential social bots. Specifically, we collected data from X (formerly Twitter) on several news items that had a great impact in Japan, and analyzed the spread of information by social bots using a combination of existing tools and methods. Our analysis compared the number and percentage of social bots on X in Japanese cases to existing research analyzing those during the 2016 US presidential election, and clarified what kind of social bots are influencing the information diffusion. In all cases we examined, our analysis showed that social bot activity in Japan was more active than during the 2016 US presidential election. We also found that humans are spreading posts created by social bots, as was the case during the 2016 US presidential election. Furthermore, we confirmed that the characteristics of social bots reposted by humans on X in Japan are similar to human accounts, and it is difficult to detect them using only the profile information on the X account page.

Security analysis of a web system is complicated, and thus analysis using formal methods to describe system specification mathematically has attracted attention. Some previous studies have adopted formal methods, but their models cannot express parallel communication completely. This limitation gives rise to problems where web functions, such as a cache that stores contents, cannot be defined and attacks that forge contents cannot be analyzed. These problems are present in the Alloy-based implementations of current models that do not have the ability to express temporal logic. Therefore, we design implementation and evaluation of temporal logic in Alloy to express time series and parallel computation for web security analysis. In doing so, state transitions in the web can be expressed by fitting them in our proposed syntax. As concrete applications, we describe a web security model that includes caches and show that our proposed syntax can analyze state-of-the-art attacks, such as unauthorized access to users' account pages via caches. The source code of our proposed model in Alloy is publicly available.

Vulnerability and attack information must be collected to assess the severity of vulnerabilities and prioritize countermeasures against cyberattacks quickly and accurately. Common Vulnerabilities and Exposures is a dictionary that lists vulnerabilities and incidents, while Common Attack Pattern Enumeration and Classification is a dictionary of attack patterns. Direct identification of common attack pattern enumeration and classification from common vulnerabilities and exposures is difficult, as they are not always directly linked. Here, an approach to directly find common links between these dictionaries is proposed. Then, several patterns, which are combinations of similarity measures and popular algorithms such as term frequency–inverse document frequency, universal sentence encoder, and sentence BERT, are evaluated experimentally using the proposed approach. Specifically, two metrics, recall and mean reciprocal rank, are used to assess the traceability of the common attack pattern enumeration and classification identifiers associated with 61 identifiers for common vulnerabilities and exposures. The experiment confirms that the term frequency–inverse document frequency algorithm provides the best overall performance.

For effective vulnerability management, vulnerability and attack information must be collected quickly and efficiently. A security knowledge repository can collect such information. The Common Vulnerabilities and Exposures (CVE) provides known vulnerabilities of products, while the Common Attack Pattern Enumeration and Classification (CAPEC) stores attack patterns, which are descriptions of common attributes and approaches employed by adversaries to exploit known weaknesses. Due to the fact that the information in these two repositories are not linked, identifying related CAPEC attack information from CVE vulnerability information is challenging. Currently, the related CAPEC-ID can be traced from the CVE-ID using Common Weakness Enumeration (CWE) in some but not all cases. Here, we propose a method to automatically trace the related CAPEC-IDs from CVE-ID using three similarity measures: TF–IDF, Universal Sentence Encoder (USE), and Sentence-BERT (SBERT). We prepared and used 58 CVE-IDs as test input data. Then, we tested whether we could trace CAPEC-IDs related to each of the 58 CVE-IDs. Additionally, we experimentally confirm that TF–IDF is the best similarity measure, as it traced 48 of the 58 CVE-IDs to the related CAPEC-ID.

Security patterns encompass security-related issues in secure software system development and operations that often appear in certain contexts. Since the late 1990s, about 500 security patterns have been proposed. Although the technical components are well investigated, the direction, overall picture, and barriers to implementation are not. Here, a systematic literature review of 240 papers is used to devise a taxonomy for security pattern research. Our taxonomy and the survey results should improve communications among practitioners and researchers, standardize the terminology, and increase the effectiveness of security patterns.

OBJECTIVE: We performed a retrospective analysis of commercial drivers to clarify the background of incidents of sudden illness while driving. METHODS: The analysis used reports submitted by employers to the Japan Ministry of Land, Infrastructure, Transport, and Tourism regarding commercial drivers who had been ordered to stop driving owing to health problems. RESULTS: Of 211 cases with an average work history of 15.2 years, there were 88 bus drivers, 70 taxi drivers, and 53 truck drivers, 36.0% of who had died as a result of their disease. Among taxi and truck drivers, more than 70% of incidents were due to cardiac, aortic, and cerebrovascular disease. More than 80% of these were unable to avoid traffic accidents caused by sudden illness. However, among bus drivers, cardiac, aortic, and cerebrovascular disease accounted for only 23.5% of incidents, and accidents were avoided in more than half of the cases. The duration between starting work and the incident time was significantly shorter among bus drivers [mean 3.3 hours, standard deviation (SD) 3.1] than taxi (7.7 hours, SD 5.8) and truck (7.2 hours, SD 6.3) drivers (P

Security patterns are intended to support software developers as the patterns encapsulate security expert knowledge. However, these patterns may be inappropriately applied because most developers are not security experts, leading to threats and vulnerabilities. Here we propose a support method for security design patterns in the implementation phase of software development. Our method creates a test template from a security design pattern, consisting of an “aspect test template” to observe the internal processing and a “test case template”. Providing design information creates a test from the test template with a tool. Because our test template is reusable, it can easily perform a test to validate a security design pattern. In an experiment involving four students majoring in information sciences, we confirm that our method can realize an effective test, verify pattern applications, and support pattern implementation.