| 2026 | ICAART | AISTIP: AI Security Threat Intelligence Platform to Gather Knowledge from Technical Documents. | Kento Hasegawa, Seira Hidano |
| 2025 | IWSEC | Evaluation of Adversarial Input Attacks in Retrieval-Augmented Generation Using Large Language Models. | Kento Hasegawa, Seira Hidano, Kazuhide Fukushima |
| 2024 | ICISSP | Vulnerability Information Sharing Platform for Securing Hardware Supply Chains. | Kento Hasegawa, Katsutoshi Hanahara, Hiroshi Sugisaki, Minoru Kozu, Kazuhide Fukushima, Yosuke Murakami, Shinsaku Kiyomoto |
| 2024 | ICISSP | PenGym: Pentesting Training Framework for Reinforcement Learning Agents. | Huynh Phuong Thanh Nguyen, Zhi Chen, Kento Hasegawa, Kazuhide Fukushima, Razvan Beuran |
| 2024 | ICMLA | RAG Certainty: Quantifying the Certainty of Context-Based Responses by LLMs. | Kento Hasegawa, Seira Hidano, Kazuhide Fukushima |
| 2023 | ICISSP | Automating XSS Vulnerability Testing Using Reinforcement Learning. | Kento Hasegawa, Seira Hidano, Kazuhide Fukushima |
| 2023 | TrustCom | Membership Inference Attacks against GNN-based Hardware Trojan Detection. | Kento Hasegawa, Kazuki Yamashita, Seira Hidano, Kazuhide Fukushima, Kazuo Hashimoto, Nozomu Togawa |
| 2022 | IOLTS | Effective Hardware-Trojan Feature Extraction Against Adversarial Attacks at Gate-Level Netlists. | Kazuki Yamashita, Tomohiro Kato, Kento Hasegawa, Seira Hidano, Kazuhide Fukushima, Nozomu Togawa |
| 2021 | CANS | Toward Learning Robust Detectors from Imbalanced Datasets Leveraging Weighted Adversarial Training. | Kento Hasegawa, Seira Hidano, Shinsaku Kiyomoto, Nozomu Togawa |
| 2021 | IOLTS | Data Augmentation for Machine Learning-Based Hardware Trojan Detection at Gate-Level Netlists. | Kento Hasegawa, Seira Hidano, Kohei Nozawa, Shinsaku Kiyomoto, Nozomu Togawa |
| 2020 | ASPDAC | FPGA-based Heterogeneous Solver for Three-Dimensional Routing. | Kento Hasegawa, Ryota Ishikawa, Makoto Nishizawa, Kazushi Kawamura, Masashi Tawada, Nozomu Togawa |
| 2020 | IOLTS | Evaluation on Hardware-Trojan Detection at Gate-Level IP Cores Utilizing Machine Learning Methods. | Tatsuki Kurihara, Kento Hasegawa, Nozomu Togawa |
| 2020 | IOLTS | An Anomalous Behavior Detection Method for IoT Devices by Extracting Application-Specific Power Behaviors. | Kazunari Takasaki, Kento Hasegawa, Ryoichi Kida, Nozomu Togawa |
| 2019 | ESORICS | Adversarial Examples for Hardware-Trojan Detection at Gate-Level Netlists. | Kohei Nozawa, Kento Hasegawa, Seira Hidano, Shinsaku Kiyomoto, Kazuo Hashimoto, Nozomu Togawa |
| 2019 | IOLTS | Empirical Evaluation on Anomaly Behavior Detection for Low-Cost Micro-Controllers Utilizing Accurate Power Analysis. | Kento Hasegawa, Kiyoshi Chikamatsu, Nozomu Togawa |
| 2018 | IOLTS | Detecting the Existence of Malfunctions in Microcontrollers Utilizing Power Analysis. | Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa |
| 2018 | ISCAS | A Trojan-invalidating Circuit Based on Signal Transitions and Its FPGA Implementation. | Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa |
| 2018 | TrustCom | Hardware Trojan Detection Utilizing Machine Learning Approaches. | Kento Hasegawa, Youhua Shi, Nozomu Togawa |
| 2017 | IOLTS | Hardware Trojans classification for gate-level netlists using multi-layer neural networks. | Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa |
| 2017 | ISCAS | Trojan-feature extraction at gate-level netlists and its application to hardware-Trojan detection using random forest classifier. | Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa |
| 2016 | IOLTS | Hardware Trojans classification for gate-level netlists based on machine learning. | Kento Hasegawa, Masaru Oya, Masao Yanagisawa, Nozomu Togawa |