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ORCIDKento Hasegawa
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2020 – today
- 2025
[j9]Huynh Phuong Thanh Nguyen
, Kento Hasegawa, Kazuhide Fukushima, Razvan Beuran:
PenGym: Realistic training environment for reinforcement learning pentesting agents. Comput. Secur. 148: 104140 (2025)- 2024
- [c27]Kento Hasegawa, Seira Hidano, Kazuhide Fukushima:
AutoRed: Automating Red Team Assessment via Strategic Thinking Using Reinforcement Learning. CODASPY 2024: 325-336 - [c26]Kento Hasegawa, Katsutoshi Hanahara, Hiroshi Sugisaki, Minoru Kozu, Kazuhide Fukushima, Yosuke Murakami, Shinsaku Kiyomoto:
Vulnerability Information Sharing Platform for Securing Hardware Supply Chains. ICISSP 2024: 403-410 - [c25]Huynh Phuong Thanh Nguyen, Zhi Chen, Kento Hasegawa, Kazuhide Fukushima, Razvan Beuran:
PenGym: Pentesting Training Framework for Reinforcement Learning Agents. ICISSP 2024: 498-509 - [c24]Hibiki Nakanishi, Kota Hisafuru, Kento Hasegawa, Seira Hidano, Kazuhide Fukushima, Kazuo Hashimoto, Nozomu Togawa:
Initial Seeds Generation Using LLM for IoT Device Fuzzing. IOTSMS 2024: 5-10 - [c23]Yuka Ikegami, Ryotaro Negishi, Kento Hasegawa, Seira Hidano, Kazuhide Fukushima, Kazuo Hashimoto, Nozomu Togawa:
Prioritizing Vulnerability Assessment Items Using LLM Based on IoT Device Documentations. IOTSMS 2024: 147-152 - [c22]Hiroya Kato, Kento Hasegawa, Seira Hidano, Kazuhide Fukushima:
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning. SaTML 2024: 309-326 - 2023
- [j8]Kento Hasegawa, Seira Hidano, Kohei Nozawa, Shinsaku Kiyomoto, Nozomu Togawa:
R-HTDetector: Robust Hardware-Trojan Detection Based on Adversarial Training. IEEE Trans. Computers 72(2): 333-345 (2023) - [c21]Kento Hasegawa, Seira Hidano, Kazuhide Fukushima:
Automating XSS Vulnerability Testing Using Reinforcement Learning. ICISSP 2023: 70-80 - [c20]Kento Hasegawa, Kazuki Yamashita, Seira Hidano, Kazuhide Fukushima, Kazuo Hashimoto, Nozomu Togawa:
Membership Inference Attacks against GNN-based Hardware Trojan Detection. TrustCom 2023: 1222-1229 - [i3]Hiroya Kato, Kento Hasegawa, Seira Hidano, Kazuhide Fukushima:
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning. CoRR abs/2312.07022 (2023) - 2022
- [c19]Kazuki Yamashita, Tomohiro Kato, Kento Hasegawa, Seira Hidano, Kazuhide Fukushima, Nozomu Togawa:
Effective Hardware-Trojan Feature Extraction Against Adversarial Attacks at Gate-Level Netlists. IOLTS 2022: 1-7 - [i2]Kento Hasegawa, Seira Hidano, Kohei Nozawa, Shinsaku Kiyomoto, Nozomu Togawa:
R-HTDetector: Robust Hardware-Trojan Detection Based on Adversarial Training. CoRR abs/2205.13702 (2022) - 2021
- [j7]Kento Hasegawa, Tomotaka Inoue, Nozomu Togawa:
A Two-Stage Hardware Trojan Detection Method Considering the Trojan Probability of Neighbor Nets. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 104-A(11): 1516-1525 (2021) - [j6]Kohei Nozawa, Kento Hasegawa, Seira Hidano, Shinsaku Kiyomoto, Kazuo Hashimoto, Nozomu Togawa:
Generating Adversarial Examples for Hardware-Trojan Detection at Gate-Level Netlists. J. Inf. Process. 29: 236-246 (2021) - [c18]Kento Hasegawa, Seira Hidano, Shinsaku Kiyomoto, Nozomu Togawa:
Toward Learning Robust Detectors from Imbalanced Datasets Leveraging Weighted Adversarial Training. CANS 2021: 392-411 - [c17]Kento Hasegawa, Seira Hidano, Kohei Nozawa, Shinsaku Kiyomoto, Nozomu Togawa:
Data Augmentation for Machine Learning-Based Hardware Trojan Detection at Gate-Level Netlists. IOLTS 2021: 1-4 - [i1]Kento Hasegawa, Kazuki Yamashita, Seira Hidano, Kazuhide Fukushima, Kazuo Hashimoto, Nozomu Togawa:
Node-wise Hardware Trojan Detection Based on Graph Learning. CoRR abs/2112.02213 (2021) - 2020
- [j5]Makoto Nishizawa, Kento Hasegawa, Nozomu Togawa:
A Capacitance Measurement Device for Running Hardware Devices and Its Evaluations. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 103-A(9): 1018-1027 (2020) - [j4]Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa:
Trojan-Net Classification for Gate-Level Hardware Design Utilizing Boundary Net Structures. IEICE Trans. Inf. Syst. 103-D(7): 1618-1622 (2020) - [c16]Kento Hasegawa, Ryota Ishikawa, Makoto Nishizawa, Kazushi Kawamura, Masashi Tawada, Nozomu Togawa:
FPGA-based Heterogeneous Solver for Three-Dimensional Routing. ASP-DAC 2020: 11-12 - [c15]Tatsuki Kurihara, Kento Hasegawa, Nozomu Togawa:
Evaluation on Hardware-Trojan Detection at Gate-Level IP Cores Utilizing Machine Learning Methods. IOLTS 2020: 1-4 - [c14]Kazunari Takasaki, Kento Hasegawa, Ryoichi Kida, Nozomu Togawa:
An Anomalous Behavior Detection Method for IoT Devices by Extracting Application-Specific Power Behaviors. IOLTS 2020: 1-4
2010 – 2019
- 2019
- [c13]Kohei Nozawa, Kento Hasegawa, Seira Hidano, Shinsaku Kiyomoto, Kazuo Hashimoto, Nozomu Togawa:
Adversarial Examples for Hardware-Trojan Detection at Gate-Level Netlists. CyberICPS/SECPRE/SPOSE/ADIoT@ESORICS 2019: 341-359 - [c12]Kento Hasegawa, Kazunari Takasaki, Makoto Nishizawa, Ryota Ishikawa, Kazushi Kawamura, Nozomu Togawa:
Implementation of a ROS-Based Autonomous Vehicle on an FPGA Board. FPT 2019: 457-460 - [c11]Kento Hasegawa, Kiyoshi Chikamatsu, Nozomu Togawa:
Empirical Evaluation on Anomaly Behavior Detection for Low-Cost Micro-Controllers Utilizing Accurate Power Analysis. IOLTS 2019: 54-57 - 2018
- [j3]Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa:
Empirical Evaluation and Optimization of Hardware-Trojan Classification for Gate-Level Netlists Based on Multi-Layer Neural Networks. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 101-A(12): 2320-2326 (2018) - [c10]Makoto Nishizawa, Kento Hasegawa, Nozomu Togawa:
Capacitance Measurement of Running Hardware Devices and its Application to Malicious Modification Detection. APCCAS 2018: 362-365 - [c9]Tomotaka Inoue, Kento Hasegawa, Yuki Kobayashi, Masao Yanagisawa, Nozomu Togawa:
Designing Subspecies of Hardware Trojans and Their Detection Using Neural Network Approach. ICCE-Berlin 2018: 1-4 - [c8]Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa:
A hardware-Trojan classification method utilizing boundary net structures. ICCE 2018: 1-4 - [c7]Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa:
Detecting the Existence of Malfunctions in Microcontrollers Utilizing Power Analysis. IOLTS 2018: 97-102 - [c6]Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa:
A Trojan-invalidating Circuit Based on Signal Transitions and Its FPGA Implementation. ISCAS 2018: 1-5 - [c5]Kento Hasegawa, Youhua Shi, Nozomu Togawa:
Hardware Trojan Detection Utilizing Machine Learning Approaches. TrustCom/BigDataSE 2018: 1891-1896 - 2017
- [j2]Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa:
A Hardware-Trojan Classification Method Using Machine Learning at Gate-Level Netlists Based on Trojan Features. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 100-A(7): 1427-1438 (2017) - [j1]Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa:
Trojan-Net Feature Extraction and Its Application to Hardware-Trojan Detection for Gate-Level Netlists Using Random Forest. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 100-A(12): 2857-2868 (2017) - [c4]Tomotaka Inoue, Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa:
Designing hardware trojans and their detection based on a SVM-based approach. ASICON 2017: 811-814 - [c3]Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa:
Hardware Trojans classification for gate-level netlists using multi-layer neural networks. IOLTS 2017: 227-232 - [c2]Kento Hasegawa, Masao Yanagisawa, Nozomu Togawa:
Trojan-feature extraction at gate-level netlists and its application to hardware-Trojan detection using random forest classifier. ISCAS 2017: 1-4 - 2016
- [c1]Kento Hasegawa, Masaru Oya, Masao Yanagisawa, Nozomu Togawa:
Hardware Trojans classification for gate-level netlists based on machine learning. IOLTS 2016: 203-206
Coauthor Index
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