arXiv subjects

Masaki Hashimoto

Publications and source records attributed to Masaki Hashimoto.

6 recordsLinked to original sources

arXiv2026

TOMOYO Linux: A Mandatory Access Control Method Based on Application Execution State

Existing access control methods grant access requests based on the combinations of applications as subject and files as objects. Therefore intents of applications and the possible effects caused by granting the access requests have not been taken into consideration. In this paper, we propose a new access control method based on application history and intents. With our access control method, system administrators can reduce the risks caused by malicious access attempts and wrong operations. In this paper, the concept and implementation design will be explained as well as the brief evaluation report of TOMOYO Linux, our implementation of the new access control method to Linux.

cs.OS↗

arXiv2026

Policy Description Language for Authorization using Logic-Based Programming

Recently, with the impossibility of eradicating the vulnerabilities of information systems, we must prepare for the occurrence of the security incident by the multi-layer defense called the Defense-in-Depth strategy. In the multi-layer defense, it is important to authorize accesses in fine-grained granularity to compose each layer effectively, and many access control models are proposed to follow them. However, policy description languages proposed so far cannot express the models appropriately in proper granularity. In this paper, we propose a policy description language which can designate many kinds of conditions for access control, such as the dynamic status of an application process, as an element of decision data, and implement it in Datalog. Using the proposed language, we compose the policy of SELinux, which is a major implementation achieving the multi-layer defense, and we confirm the advantages of the proposed language by evaluating its validity and expressiveness.

cs.CR↗

arXiv2026

Foundational Study on Authorship Attribution of Japanese Web Reviews for Actor Analysis

This study investigates the applicability of authorship attribution based on stylistic features to support actor analysis in threat intelligence. As a foundational step toward future application to dark web forums, we conducted experiments using Japanese review data from clear web sources. We constructed datasets from Rakuten Ichiba reviews and compared four methods: TF-IDF with logistic regression (TF-IDF+LR), BERT embeddings with logistic regression (BERT-Emb+LR), BERT fine-tuning (BERT-FT), and metric learning with $k$-nearest neighbors (Metric+kNN). Results showed that BERT-FT achieved the best performance; however, training became unstable as the number of authors scaled to several hundred, where TF-IDF+LR proved superior in terms of accuracy, stability, and computational cost. Furthermore, Top-$k$ evaluation demonstrated the utility of candidate screening, and error analysis revealed that boilerplate text, topic dependency, and short text length were primary factors causing misclassification.

cs.CL↗

arXiv2025

Vulnerability Management Chaining: An Integrated Framework for Efficient Cybersecurity Risk Prioritization

As the number of Common Vulnerabilities and Exposures (CVE) continues to grow exponentially, security teams face increasingly difficult decisions about prioritization. Current approaches using Common Vulnerability Scoring System (CVSS) scores produce overwhelming volumes of high-priority vulnerabilities, while Exploit Prediction Scoring System (EPSS) and Known Exploited Vulnerabilities (KEV) catalog offer valuable but incomplete perspectives on actual exploitation risk. We present Vulnerability Management Chaining, a decision tree framework that systematically integrates these three approaches to achieve efficient vulnerability prioritization. Our framework employs a two-stage evaluation process: first applying threat-based filtering using KEV membership or EPSS threshold $\geq$ 0.088), then applying vulnerability severity assessment using CVSS scores $\geq$ 7.0) to enable informed deprioritization. Experimental validation using 28,377 real-world vulnerabilities and vendor-reported exploitation data demonstrates 18-fold efficiency improvements while maintaining 85.6\% coverage. Organizations can reduce urgent remediation workload by approximately 95\%. The integration identifies 48 additional exploited vulnerabilities that neither KEV nor EPSS captures individually. Our framework uses exclusively open-source data, enabling immediate adoption regardless of organizational resources.

cs.CR↗

arXiv2025

Uncovering Black-hat SEO based fake E-commerce scam groups from their redirectors and websites

While law enforcements agencies and cybercrime researchers are working hard, fake E-commerce scam is still a big threat to Internet users. One of the major techniques to victimize users is luring them by black-hat search-engine-optimization (SEO); making search engines display their lure pages as if these were placed on compromised websites and then redirecting visitors to malicious sites. In this study, we focus on the threat actors conduct fake E-commerce scam with this strategy. Our previous study looked at the connection between some malware families used for black-hat SEO to enlighten threat actors and their infrastructures, however it shows only a limited part of the whole picture because we could not find all SEO malware samples from limited sources. In this paper, we aim to identify and analyze threat actor groups using a large dataset of fake E-commerce sites collected by Japan Cybercrime Control Center, which we believe is of higher quality. It includes 692,865 fake EC sites gathered from redirectors over two and a half years, from May 20, 2022 to Dec. 31, 2024. We analyzed the links between these sites using Maltego, a well-known link analysis tool, and tailored programs. We also conducted time series analysis to track group changes in the groups. According to the analysis, we estimate that 17 relatively large groups were active during the dataset period and some of them were active throughout the period.

cs.CR↗

arXiv2023

An Analysis of Socialbots Activity and Influence in Modern Japanese Social Media

In recent years, the proliferation of disinformation has become an issue against the backdrop of the spread of social media. In this study, we focus on socialbots, one of the causes of this problem, and analyze several domestic cases to clarify the actual activities and influence of socialbots. As a result of this analysis, we found that the influence of socialbots is greater in Japan than in the U.S. presidential election of 2016, which is a representative case of socialbot influence, and that socialbots retweeted by humans are not significantly different from human accounts. In addition, socialbot accounts retweeted by humans are not significantly different from human accounts. This paper also discusses specific methods and perspectives for further analysis and research on the influence of socialbots.

cs.SI↗