| Project/Area Number |
18K11299
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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 60070:Information security-related
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| Research Institution | Hiroshima City University |
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Principal Investigator |
Inoue Hiroyuki 広島市立大学, 情報科学研究科, 准教授 (60468296)
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| Project Period (FY) |
2018-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | 車載ネットワーク / 組込みセキュリティ / CAN / セキュリティゲートウェイ / なりすまし / ファジング / 車載セキュリティ / ネットワークセキュリティ / CANプロトコル / 鍵配布 |
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| Outline of Final Research Achievements |
I analyzed cases of attacks via the external interface of the connected car, and examined the countermeasures. I realized the dynamic filtering mechanism with software, and evaluated it with the actual vehicle. It is possible to classify attack patterns in in-vehicle systems into several categories and provide them as standard data sets. I confirmed that fuzzing data can be generated by applying an algorithm called machine learning model r-VAE, and evaluated the effectiveness as attacks. I also devised a compression algorithm for CAN messages of in-vehicle LAN data, and completed a prototype that can be analyzed on a server and visualized as meaningful information such as speed and engine status.
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| Academic Significance and Societal Importance of the Research Achievements |
コネクティッドカーと呼ばれる広域ネットワークに常時接続されるような自動車や自動運転車の普及に伴い，外部からの攻撃や潜在的な脅威が高まっている．自動車に搭載されるコンピュータ同士が通信を行うための車載ネットワークにおける不正アクセスに対する防御および認証の手法について，機械学習を使用した動的なフィルタリングや脆弱性評価のためのファジングデータの生成，またクラウドへ送信する際の高効率なCANデータ圧縮方式等についてプロトタイプの開発を行った．
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