研究者業績

Tomohiro Morikawa (Bo Sun)

  (森川 智博(孫 博))

Profile Information

Affiliation
Graduate School of Applied Informatics, University of Hyogo
Degree
Doctor of Philosophy in Engineering(Feb, 2018, Waseda University)

J-GLOBAL ID
201601019783857916
researchmap Member ID
7000017476

日本国籍取得に伴い、2021年2月より名前が変わりました.


Papers

 29
  • Fabien Charmet, Tomohiro Morikawa, Akira Tanaka, Takeshi Takahashi
    ACM Transactions on Internet Technology, May 6, 2024  Peer-reviewedCorresponding author
    Phishing attacks reached a record high in 2022, as reported by the Anti-Phishing Work Group [1], following an upward trend accelerated during the pandemic. Attackers employ increasingly sophisticated tools in their attempts to deceive unaware users into divulging confidential information. Recently, the research community has turned to the utilization of screenshots of legitimate and malicious websites to identify the brands that attackers aim to impersonate. In the field of Computer Vision, convolutional neural networks (CNNs) have been employed to analyze the visual rendering of websites, addressing the problem of phishing detection. However, along with the development of these new models, arose the need to understand their inner workings and the rationale behind each prediction. Answering the question, “How is this website attempting to steal the identity of a well-known brand?” becomes crucial when protecting end-users from such threats. In cybersecurity, the application of explainable AI (XAI) is an emerging approach that aims to answer such questions. In this paper, we propose VORTEX, a phishing website detection solution equipped with the capability to explain how a screenshot attempts to impersonate a specific brand. We conduct an extensive analysis of XAI methods for the phishing detection problem and demonstrate that VORTEX provides meaningful explanations regarding the detection results. Additionally, we evaluate the robustness of our model against Adversarial Example attacks. We adapt these attacks to the VORTEX architecture and evaluate their efficacy across multiple models and datasets. Our results show that VORTEX achieves superior accuracy compared to previous models, and learns semantically meaningful patterns to provide actionable explanations about phishing websites. Finally, VORTEX demonstrates an acceptable level of robustness against adversarial example attacks.
  • Jiaxing Zhou, Tao Ban, Tomohiro Morikawa, Takeshi Takahashi, Daisuke Inoue
    2023 IEEE Symposium on Computers and Communications (ISCC), Jul 9, 2023  Peer-reviewed
  • Yu-Wei Chang, Hong-Yen Chen, Chansu Han, Tomohiro Morikawa, Takeshi Takahashi, Tsung-Nan Lin
    IEEE Transactions on Emerging Topics in Computing, 1-16, 2023  Peer-reviewed
  • Chansu Han, Akira Tanaka, Jun’ichi Takeuchi, Takeshi Takahashi, Tomohiro Morikawa, Tsung-Nan Lin
    Proceedings of the 9th International Conference on Information Systems Security and Privacy, 2023  Peer-reviewed
  • Mitsuhiro Umizaki, Tomohiro Morikawa, Akira Fujita, Takeshi Takahashi, Tsung-Nan Lin, Daisuke Inoue
    2022 IEEE Symposium on Computers and Communications (ISCC), Jun 30, 2022  Peer-reviewed

Misc.

 6