A paper got accepted!

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.

A paper got accepted!

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 has been published in Vol. 34, pp. 748–774 (September 2026) and is available on J-STAGE.

Yuna 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, pp. 748–774, Sep 2026. [J-STAGE]

A paper got accepted!

We are thrilled to announce that our workshop paper entitled “Large-Scale Analysis of Malware Distribution via Fake Game Cheats and Cracked Software on YouTube” has been accepted for publication in the 5th Workshop on Attackers and Cyber-Crime Operations (WACCO 2026), co-located with IEEE EuroS&P 2026. Congratulations Yamagishi-kun!

In this work, we present VIPER (VIdeo Platform Exploitation Reconnaissance), an automated system that continuously monitors YouTube for malware distribution campaigns masquerading as game cheats or cracked software—a class of threat we term VidCrackBait. VIPER iteratively updates its search queries to track evolving attack patterns and harvests video metadata via the YouTube Data API v3. Over five months, VIPER collected 104,974 videos from 38,855 unique channels, identifying 6,976 distinct fully qualified domain names embedded in descriptions and comments. Our analysis reveals that attackers target popular competitive games with recurring title/description/thumbnail patterns, and we identify 396 intermediate YouTube channels that post no content but serve as persistent relay nodes—forming multi-stage redirection chains that link YouTube to messaging platforms, file-hosting services, and social media.

Rei Yamagishi, Shota Fujii, and Tatsuya Mori, "Large-Scale Analysis of Malware Distribution via Fake Game Cheats and Cracked Software on YouTube." In Proc. of the 5th Workshop on Attackers and Cyber-Crime Operations (WACCO 2026), co-located with IEEE EuroS&P 2026, Venice, Italy, Jun 2026.

A paper got accepted!

We are thrilled to announce that our conference paper entitled “Cross-Lingual Vulnerabilities of Text-to-Image Models: Evaluating Data Poisoning Attacks Across Ten Languages” has been accepted for publication in the 28th International Conference on Pattern Recognition (ICPR 2026). Congraturations Kakebayashi-kun!

Ryohei Kakebayashi and Tatsuya Mori, "Cross-Lingual Vulnerabilities of Text-to-Image Models: Evaluating Data Poisoning Attacks Across Ten Languages." In Proc. of the 28th International Conference on Pattern Recognition (ICPR 2026) Lyon, France, Sep 2026. Published in Springer LNCS.

A paper got accepted!

We are thrilled to announce that our conference paper entitled “TEV-IDS: CAN Intrusion Detection System via Spatial Temporal-Entropy-Variation Fingerprinting” has been accepted for publication in IEEE 103rd Vehicular Technology Conference (VTC2026-Spring). Congratulations to Peng-kun and kudos to the entire team!

In this work, we propose TEV-IDS, an ID-agnostic intrusion detection framework for in-vehicle CAN networks. Existing systems are largely limited to binary benign/attack decisions and depend on vehicle-specific CAN ID semantics, which hinders both fine-grained attack diagnosis and scalable deployment across heterogeneous platforms. TEV-IDS overcomes these challenges by constructing a Temporal–Entropy–Variation (TEV) feature space from ID-independent cues (inter-arrival intervals, payload entropy, and bit-level variation), and projecting them via a Tri-Perspective View (TPV) into compact 2D feature maps for a lightweight classifier. A stratified few-shot adaptation protocol further enables calibration to unseen vehicles with minimal target data. On the CAN-MIRGU dataset, TEV-IDS achieves a macro-F1 above 0.99 for 17-class attack classification, while cross-domain evaluation on the ROAD dataset yields a macro-AUC above 0.924 with limited calibration data, all under real-time CPU inference latency.

Shuo Peng, Zhihe Zhang, Go Tsuruoka, and Tatsuya Mori. "TEV-IDS: CAN Intrusion Detection System via Spatial Temporal-Entropy-Variation Fingerprinting." In Proc. of IEEE 103rd Vehicular Technology Conference (VTC2026-Spring), Nice, France, Jun 2026.