M
Masashi Eto
Researcher at National Institute of Information and Communications Technology
Publications - 46
Citations - 715
Masashi Eto is an academic researcher from National Institute of Information and Communications Technology. The author has contributed to research in topics: Malware & Darknet. The author has an hindex of 13, co-authored 45 publications receiving 626 citations. Previous affiliations of Masashi Eto include Inha University & Unitec Institute of Technology.
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Proceedings ArticleDOI
Statistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation
TL;DR: A new evaluation dataset, called Kyoto 2006+, built on the 3 years of real traffic data which are obtained from diverse types of honeypots which will greatly contribute to IDS researchers in obtaining more practical, useful and accurate evaluation results.
Proceedings ArticleDOI
nicter: An Incident Analysis System Toward Binding Network Monitoring with Malware Analysis
Daisuke Inoue,Masashi Eto,Katsunari Yoshioka,S. Baba,K. Suzuki,Junji Nakazato,Kazuhiro Ohtaka,Koji Nakao +7 more
TL;DR: A brief overview of the nicter is described, and possible contributions to the worldwide observatory of malicious behavior and attack tools (WOMBAT) are described.
Proceedings ArticleDOI
Malware Behavior Analysis in Isolated Miniature Network for Revealing Malware's Network Activity
TL;DR: A novel way to analyze malware behavior is proposed: focus closely on the malware's external behavior, which is totally isolated from the real Internet and causes no further unwanted propagation.
Journal ArticleDOI
Practical Correlation Analysis between Scan and Malware Profiles against Zero-Day Attacks Based on Darknet Monitoring
TL;DR: Inter-relationship between above two types of profiles is practically discussed and studied so that frequently observed malwares behaviors can be finally identified in view of scan-malware chain.
Journal ArticleDOI
Automated Malware Analysis System and Its Sandbox for Revealing Malware's Internal and External Activities
TL;DR: A novel way to analyze malware is proposed: focus closely on the malware's external activity toward the network to correlate with a security incident.