Visible to the public Biblio

Filters: Author is Kitagawa, Naoya  [Clear All Filters]
2023-02-03
Suzumura, Toyotaro, Sugiki, Akiyoshi, Takizawa, Hiroyuki, Imakura, Akira, Nakamura, Hiroshi, Taura, Kenjiro, Kudoh, Tomohiro, Hanawa, Toshihiro, Sekiya, Yuji, Kobayashi, Hiroki et al..  2022.  mdx: A Cloud Platform for Supporting Data Science and Cross-Disciplinary Research Collaborations. 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech). :1–7.
The growing amount of data and advances in data science have created a need for a new kind of cloud platform that provides users with flexibility, strong security, and the ability to couple with supercomputers and edge devices through high-performance networks. We have built such a nation-wide cloud platform, called "mdx" to meet this need. The mdx platform's virtualization service, jointly operated by 9 national universities and 2 national research institutes in Japan, launched in 2021, and more features are in development. Currently mdx is used by researchers in a wide variety of domains, including materials informatics, geo-spatial information science, life science, astronomical science, economics, social science, and computer science. This paper provides an overview of the mdx platform, details the motivation for its development, reports its current status, and outlines its future plans.
2020-02-10
Dan, Kenya, Kitagawa, Naoya, Sakuraba, Shuji, Yamai, Nariyoshi.  2019.  Spam Domain Detection Method Using Active DNS Data and E-Mail Reception Log. 2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). 1:896–899.

E-mail is widespread and an essential communication technology in modern times. Since e-mail has problems with spam mails and spoofed e-mails, countermeasures are required. Although SPF, DKIM and DMARC have been proposed as sender domain authentication, these mechanisms cannot detect non-spoofing spam mails. To overcome this issue, this paper proposes a method to detect spam domains by supervised learning with features extracted from e-mail reception log and active DNS data, such as the result of Sender Authentication, the Sender IP address, the number of each DNS record, and so on. As a result of the experiment, our method can detect spam domains with 88.09% accuracy and 97.11% precision. We confirmed that our method can detect spam domains with detection accuracy 19.40% higher than the previous study by utilizing not only active DNS data but also e-mail reception log in combination.