We are thrilled to announce that our conference paper entitled “Detecting Phishing on Shared-Domain Hosting Services Using LLM-Based Contextual Mismatch Reasoning” has been accepted for publication in The 21st International Conference on Availability, Reliability and Security (ARES 2026). Congratulations to Inuzuka-kun and kudos to the entire team!

In this work, we propose an LLM-based phishing detection workflow tailored to shared-domain hosting services (SHSs) such as free website builders, where benign and malicious pages coexist under the same provider-managed domain and conventional URL- and domain-based features break down. Our three-stage pipeline — Brand Analyzer, Content Analyzer, and Judge — reasons about the contextual mismatch between the brand a page claims to represent and the sensitive actions it prompts. Evaluated across ten SHSs with both commercial (GPT-4.1-mini) and open-source (Qwen3-8B) models, our system achieves high recall, outperforms state-of-the-art baselines including FreePhish and PhishLLM, and provides natural-language rationales that explain each verdict.

Sho Inuzuka, Takaaki Toda, Daiki Chiba, and Tatsuya Mori, "Detecting Phishing on Shared-Domain Hosting Services Using LLM-Based Contextual Mismatch Reasoning." In Proc. of The 21st International Conference on Availability, Reliability and Security (ARES 2026), Linköping, Sweden, Aug 2026.

We are thrilled to announce that our journal paper entitled “Comprehensive Evaluation and Defense against Adversarial Fog Attacks on LiDAR-Based Autonomous Driving” has been accepted for publication in the Journal of Information Processing (JIP). Congratulations to Tanaka-kun and kudos to the entire team!

This paper is an extended journal version of our ACM AsiaCCS 2025 paper. In this work, we present a comprehensive evaluation of Adversarial Fog Attacks (AFA), which exploit vulnerabilities of point cloud preprocessing filters in LiDAR-based autonomous driving. We systematically analyze how environmental and attack parameters affect the attack effectiveness, physically validate the attack across multiple LiDAR models, and investigate post-detection avoidance behaviors together with practical defense strategies. The paper will appear in Vol. 34 (September 2026).

Yuuna Tanaka, Kazuki Nomoto, Ryunosuke Kobayashi, Go Tsuruoka, and Tatsuya Mori, "Comprehensive Evaluation and Defense against Adversarial Fog Attacks on LiDAR-Based Autonomous Driving." Journal of Information Processing, Vol. 34, Sep 2026.