早稲田大学 · Computer Science
케핑 유 교수의 연구실은 산업인터넷(IoT), 스마트 팩토리, 스마트 그리드 등 지능형 인프라 시스템을 중심으로 정보 보안과 신뢰성 있는 데이터 관리 기반의 안전한 통합 시스템을 연구하고 있습니다. 특히 블록체인 기반 접근 제어, APT 공격 탐지, 원격 의료 및 전력망의 정보 중심 네트워킹(ICN)을 통한 보안 강화 등 실용적이고 기술적 도전 과제를 해결하는 데 초점을 맞추고 있습니다. 연구는 실세계의 복잡한 환경에서의 안정성과 실시간 성능을 확보하는 데 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
The industrial Internet of Things (IIoT) supports recent developments in data management and information services, as well as services for smart factories. Nowadays, many mature IIoT cloud platforms are available to serve smart factories. However, due to the semicredibility nature of the IIoT cloud platforms, how to achieve secure storage, access control, information update and deletion for smart factory data, as well as the tracking and revocation of malicious users has become an urgent problem
In a time of great change, accelerating globalization and increasing uncertainty, all countries, whether developed or developing, are searching for a new form of governance that is better adapted to the times so as to gain an advantage in economic competitiveness and create substantial and sustainable social growth. As governance theory is becoming the dominant political theory in response to the change, the values backing the discourse and texts consistent with them have helped revise the theor
It is expected that a mixture of autonomous and manual vehicles will persist as a part of the intelligent transportation system (ITS) for many decades. Thus, addressing the safety issues arising from this mix of autonomous and manual vehicles before autonomous vehicles are entirely popularized is crucial. As the ITS system has increased in complexity, autonomous vehicles exhibit problems such as a low intention recognition rate and poor real-time performance when predicting the driving direction
Breast cancer, the most common cancer in women, is receiving increasing attention. The lack of high-quality medical resources, especially highly skilled doctors, in remote areas makes the diagnosis of breast cancer inefficient and causes great harm to women. The emergence of remote e-health has improved the situation to a certain extent, but its capabilities are still hampered by technical limitations, which manifest in two main aspects. First, due to network bandwidth limitations, it is difficu
The Industrial Internet of Things (IIoT) is a physical information system developed based on traditional industrial control networks. As one of the most critical infrastructure systems, IIoT is also a preferred target for adversaries engaged in advanced persistent threats (APTs). To address this issue, we explore a deep-learning-based proactive APT detection scheme in IIoT. In this scheme, considering the characteristics of long attack sequences and long-term continuous APT attacks, our solution
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The Industrial Internet of Things (IIoT), a typical Internet of Things (IoT) application, integrates the global industrial system with other advanced computing, analysis, and sensing technologies through Internet connectivity. Due to the limited storage and computing capacity of edge and IIoT devices, data sensed and collected by these devices are usually stored in the cloud. Encryption is commonly used to ensure privacy and confidentiality of IIoT data. However, the key used for data encryption
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