A study on security and vulnerability management of facility networks
| Project/Area Number |
18K11257
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Review Section |
Basic Section 60060:Information network-related
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| Research Institution | The University of Tokyo |
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Principal Investigator |
Ochiai Hideya 東京大学, 大学院情報理工学系研究科, 准教授 (10615652)
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| Project Period (FY) |
2018-04-01 – 2022-03-31
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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,560,000 (Direct Cost: ¥1,200,000、Indirect Cost: ¥360,000)
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| Keywords | IoT / セキュリティ / 設備ネットワーク / LAN Security / OT Security / LAN / サイバーセキュリティ |
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| Outline of Final Research Achievements |
We developed an algorithm for learning and controlling communication flows for improving the security of IoT device connected networks. We also developed anomaly detection devices for local area networks (LANs) based on our own new method, and deployed 50 devices around the regions of South East Asia where are suffuring from severe malware encounter rates. We could successfully make a taxonomy of suspicious LAN internal device-to-device communications and visualization of them with the collected events. We further developed another anomaly detection scheme for improving the physical security of facility networks where are connected at the edges of IoT systems.
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| Academic Significance and Societal Importance of the Research Achievements |
ネットワーク・セキュリティの研究は、これまでネットワークの上流での異常検知や管理が一般的であったが、本研究はIoTを取り巻く環境すなわちLAN内部の通信管理や異常検知に主眼を置いている。また本手法で確立した装置をマルウェア感染が深刻な地域に実際に多数展開し、そこからLAN内不審活動の可視化・体系化を行ったことは、これまでにない学術的な意義を持つ。
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Report
(5 results)
Research Products
(36 results)
