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Anomaly Detection of Industrial Control Systems Based on Transfer Learning

Tsinghua Science and Technology 2021年第6期26卷 821-832页

作者： Weiping Wang Zhaorong Wang Zhanfan Zhou Haixia Deng Weiliang Zhao Chunyang Wang Yongzhen Guo School of Computer and Communication Engineering the Beijing Key Laboratory of Knowledge Engineering for Materials Scienceand the Institute of Artificial IntelligenceUniversity of Science and Technology BeijingBeijing 100083China Shunde Graduate School University of Science and Technology BeijingGuangzhou 528399China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083China School of Mechanical Engineering University of Science and Technology BeijingBeijing 100083China Donlinks School of Economics and Management University of Science and Technology Beijing Beijing 100083China School of Automation Beijing Institute of TechnologyBeijing 100081 China Software Testing Center Beijing 100048China.

Industrial Control Systems(ICSs)are the lifeline of a ***,the anomaly detection of ICS traffic is an important *** paper proposes a model based on a deep residual Convolution Neural Network(CNN)to prevent gradient explosion or gradient disappearance and guarantee *** developed methodology addresses two limitations:most traditional machine learning methods can only detect known network attacks and deep learning algorithms require a long time to *** utilization of transfer learning under the modification of the existing residual CNN structure guarantees the detection of unknown ***-dimensional ICS flow data are converted into two-dimensional grayscale images to take full advantage of the features of *** show that the proposed method achieves a high score and solves the time problem associated with deep learning model *** model can give reliable predictions for unknown or differently distributed abnormal data through short-term ***,the proposed model ensures the safety of ICSs and verifies the feasibility of transfer learning for ICS anomaly detection.

关键词： anomaly detection transfer learning deep learning Industrial Control System(ICS)

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Linking Data Sovereignty and Data Economy: Arising Areas of Tension 17

Linking Data Sovereignty and Data Economy: Arising Areas of ...

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17th International Conference on Wirtschaftsinformatik, WI 2022

作者： Lauf, Florian Scheider, Simon Bartsch, Jan Herrmann, Philipp Radic, Marija Rebbert, Marcel Nemat, André T. Schlueter-Langdon, Christoph Konrad, Ralf Sunyaev, Ali Meister, Sven Fraunhofer Institute for Software and Systems Engineering ISST Healthcare Dortmund Germany TU Dortmund Chair of Industrial Information Management Dortmund Germany Department of Economics and Management Karlsruhe Germany Fraunhofer Center for International Management and Knowledge Economy IMW Corporate Development in International Competition Division Leipzig Germany Witten/Herdecke University Institute for Digital Transformation in Healthcare GmbH Witten Germany T-Systems International GmbH Digital Solutions - Data Intelligence Hub Frankfurt Germany Claremont Graduate University Drucker School of Management Claremont United States KASTEL Security Research Labs Karlsruhe Germany Witten/Herdecke University Faculty of Health School of Medicine Witten Germany

In the emerging information economy, data evolves as an essential asset and personal data in particular is used for data-driven business models. However, companies frequently leverage personal data without considering individuals’ data sovereignty. Therefore, we strive to strengthen individuals’ position in data ecosystems by combining concepts of data sovereignty and data economy. Our research design comprises an approach to design thinking iteratively generating, validating, and refining such concepts. As a result, we identified ten areas of tension that arise when linking data sovereignty and data economy. Subsequently, we propose initial solutions to resolve these tensions and thus contribute to knowledge about the development of fair data ecosystems benefiting both individuals’ sovereignty and companies’ access to data. © 2022 17th International Conference on Wirtschaftsinformatik, WI 2022. All rights reserved.

关键词： Ecosystems

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Task Coordination Organization Model and the Task Allocation Algorithm for Resource Contention of the Syncretic System

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Tsinghua Science and Technology 2016年第4期21卷 459-470页

作者： Danfeng Wu Guangping Zeng Di He Zhaopeng Qian Qingchuan Zhang School of Computer&Communication Engineering University of Science&Technology BeijingBeijing 100083China School of Software Liaoning Technical UniversityHuludao 125105China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing 100083China Graduate Center City University of New YorkNew YorkNY 10016USA

At present, robot embedded systems have some common problems such as closure and poor dynamic evolution. Aiming at resolving this situation, our paper focuses on improvements to the robot embedded system and sets up a new robot system architecture, and we propose a syncretic mechanism of a robot and SoftMan （SM）. In the syncretic system, the structural organization of the SoftMan group and its modes are particularly important in establishing the task coordination mechanism. This paper, therefore, proposes a coordination organization model based on the SoftMan group, and studies in detail the process of task allocation for resource contention, which facilitates a rational allocation of system resources. During our research, we introduced Resource Requirement Length Algorithm （RRLA） to calculate the resource requirements of the task and a resource conformity degree allocation algorithm of Resource Conformity Degree Algorithm （RCDA） for resource contention. Finally, a comparative evaluation of RCDA with five other frequently used task allocation algorithms shows that RCDA has higher success and accuracy rates with good stability and reliability.

关键词： SoftMan robot syncretic system organization model task allocation game theory

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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Local Cloud Storage: Temporal local file sharing with previously paired wireless memories and cross-layer simultaneous data transmission mechanism

Local Cloud Storage: Temporal local file sharing with previo...

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2012 IEEE 12th International Conference on Computer and Information Technology, CIT 2012

作者： Arakawa, Yutaka Tanaka, Yuta Tagashira, Shigeaki Fukuda, Akira Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Department of Knowledge Management German Research Center for Artificial Intelligence Kaiserslautern Germany Corporate Software Engineering Center Toshiba Corporation Tokyo Japan Faculty of Informatics Kansai University Osaka Japan

ISBN: (纸本)9780769548586

In this paper, we propose a novel file-sharing system called Local Cloud Storage (LCS), based on the leading-edge device called "wireless memory" such as Eye-Fi and Flash Air. Most interesting point is that wireless configuration of multiple devices is previously configured to communicate with each other. As a result, these devices are recognized as just a memory on operating systems. If a user adds a file into the mounted memory, the file is distributed to other memory via the pre-configured wireless connection unconsciously. In addition to the concept of LCS, we also propose a novel Cross-layer Simultaneous Data Transmission Mechanism (CSDTM) based on UDP broadcast in 802.11 for accelerating single-hop data simultaneous distribution between devices. Of course, a normal TCP can be adopted for data transmission in LCS. However, by adopting UDP broadcast, we can realize simultaneous data transmission to multiple receivers. Since UDP broadcast in wireless network is so unreliable, it hasn't been used for data transmission. However, by adding our designed application layer retransmission mechanism, CSDTM achieves 100% data transmission in our assumed environment. Through the performance evaluation of CSDTM, we show that it transmit data to four receivers within half the time a conventional method takes. © 2012 IEEE.

关键词： Data transfer

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WiFiTag: Direct link from the real world to online digital contents

WiFiTag: Direct link from the real world to online digital c...

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2012 7th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing, 3PGCIC 2012

作者： Arakawa, Yutaka Sonoda, Yuki Tagashira, Shigeaki Fukuda, Akira Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Department of Knowledge Management German Research Center for Artificial Intelligence Kaiserslautern Germany Corporate Software Engineering Center Toshiba Corporation Tokyo Japan Faculty of Informatics Kansai University Osaka Japan

ISBN: (纸本)9781467329910

WiFiTag is a simple and low cost solution for associating the real world to the online media such as web sites and YouTube. Our idea is to redirect a user's access based on the information of surrounding WiFi access points. Compared with WiFi-based indoor positioning system, our system requires no calculation and no much access points. This is because our system associates the digital content not with the location but with the AP itself. as the information of AP, we use ESSID, BSSID, and RSSI. by uploading a scanned WiFi information to the WiFiTag server, a terminal can get the proper URL as a response. Relationship between the WiFiTag and the URL have been defined ahead. WiFiTag realize "real to virtual" connection easily. for example, by associating lab's SSID to lab's HP, a visitor can access a lab's HP easily and directly, when the visitor comes to our lab. a camera required for QR code don't need for our system. in addition, a special card reader and touch action required for NFC are also unnecessary. If WiFi has already exist, no additional cost is necessary for our system. in this paper, we describe a overall concept of WiFiTag and show the availability and feasibility of our proposed system through some experimental evaluation results. © 2012 IEEE.

关键词： Wireless local area networks (WLAN)

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Local Cloud Storage: Temporal Local File Sharing with Previously Paired Wireless Memories and Cross-Layer Simultaneous Data Transmission Mechanism

Local Cloud Storage: Temporal Local File Sharing with Previo...

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International Conference on Computer and Information Technology (CIT)

作者： Yutaka Arakawa Yuta Tanaka Shigeaki Tagashira Akira Fukuda Department of Knowledge Management German Research Center for Artificial Intelligence Kaiserslautern Germany Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Corporate Software Engineering Center Toshiba Corporation Tokyo Japan Faculty of Informatics Kansai University Osaka Japan

ISBN: (纸本)9781467348737

关键词： Data communication Receivers IEEE 802.11 Standards Wireless communication Ad hoc networks Cloud computing Unicast

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WiFiTag: Direct Link from the Real World to Online Digital Contents

WiFiTag: Direct Link from the Real World to Online Digital C...

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International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC)

作者： Yutaka Arakawa Yuki Sonoda Shigeaki Tagashira Akira Fukuda Department of Knowledge Management German Research Center for Artificial Intelligence Kaiserslautern Germany Graduate School of Information Science and Electrical Engineering Kyushu University Fukuoka Japan Corporate Software Engineering Center Toshiba Corporation Tokyo Japan Faculty of Informatics Kansai University Osaka Japan

关键词： IEEE 802.11 Standards Servers Buildings Global Positioning System Registers Companies Google

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Optimizing relationships information in repertory grids

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IFIP Advances in Information and Communication Technology 2008年 276卷 163-172页

作者： Calot, Enrique Britos, Paola Martínez, Ramón García- Intelligent Systems Laboratory School of Engineering University of Buenos Aires Argentina Software & Knowledge Eng Center. Buenos Aires Institute of Technology Argentina Software & Knowledge Engineering Center Buenos Aires Institute of Technology Argentina

ISBN: (纸本)9780387096940

The Repertory Grid method is widely used in knowledge engineering to infer functional relationships between constructs given by an expert. The method is ignoring information that could be used to infer more precise dependencies. This paper proposes an improvement to take advantage on the information that is being ignored in the current method. Furthermore, this improvement fixes several other limitations attached to the original method, such as election in a discrete set of two values as a similarity pole or a contrast pole, the arbitrary measurement of distances, the unit-scale dependency and the normalization, among others. The idea is to use linear regression to estimate the correlation between constructs and use the fitness error as a distance measure. © 2008, IFIP International Federation for Information *** rights reserved.

关键词： Poles

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Intelligent systems applied to optimize building’s environments performance

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IFIP Advances in Information and Communication Technology 2008年 276卷 237-244页

作者： Sierra, E. Hossian, A. Rodríguez, D. García-Martínez, M. Britos, P. García-Martínez, R. Engineering School Comahue University Argentina Engineering School Comahue University Intelligent Systems Lab. FI-UBA Argentina Software and Knowledge Eng Center. Buenos Aires Institute of Technology Argentina School of Architecture University of Buenos Aires Argentina Software and Knowledge Eng. Center Buenos Aires Institute of Technology Argentina Software and Knowledge Engineering Center Buenos Aires Institute of Technology Argentina

ISBN: (纸本)9780387096940

By understanding a building as a dynamic entity capable of adapting itself not only to changing environmental conditions but also to occupant’s living habits, high standards of comfort and user satisfaction can be achieved. An intelligent system architecture integrating neural networks, expert systems and negotiating agents technologies is designed to optimize intelligent building’s performance. Results are promising and encourage further research in the field of AI applications in building automation systems. © 2008, IFIP International Federation for Information Processing. All rights reserved.

关键词： Intelligent buildings

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