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Auswahl der wissenschaftlichen Literatur zum Thema „Cyber Algorithm“
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Zeitschriftenartikel zum Thema "Cyber Algorithm"
Yin, Peng-Yeng, Fred Glover, Manuel Laguna und Jia-Xian Zhu. „A Complementary Cyber Swarm Algorithm“. International Journal of Swarm Intelligence Research 2, Nr. 2 (April 2011): 22–41. http://dx.doi.org/10.4018/jsir.2011040102.
Der volle Inhalt der QuelleLiu, Xin-Rui, Yuan Meng und Peng Chang. „Node Importance Evaluation of Cyber-Physical System under Cyber-Attacks Spreading“. Complexity 2021 (16.01.2021): 1–15. http://dx.doi.org/10.1155/2021/6641030.
Der volle Inhalt der QuelleChoi, Young Hwan, Ali Sadollah und Joong Hoon Kim. „Improvement of Cyber-Attack Detection Accuracy from Urban Water Systems Using Extreme Learning Machine“. Applied Sciences 10, Nr. 22 (18.11.2020): 8179. http://dx.doi.org/10.3390/app10228179.
Der volle Inhalt der QuelleYang, Tingting, Hailong Feng, Jian Zhao, Ruilong Deng, Ying Wang und Zhou Su. „Genetic optimization–based scheduling in maritime cyber physical systems“. International Journal of Distributed Sensor Networks 13, Nr. 7 (Juli 2017): 155014771771716. http://dx.doi.org/10.1177/1550147717717163.
Der volle Inhalt der QuelleN, Valliammal, und Barani Shaju. „Deep learning algorithm based cyber-attack detection in cyber-physical systems-a survey“. International Journal of Advanced Technology and Engineering Exploration 5, Nr. 49 (21.12.2018): 489–94. http://dx.doi.org/10.19101/ijatee.2018.547030.
Der volle Inhalt der QuelleP, Karunakaran. „Deep Learning Approach to DGA Classification for Effective Cyber Security“. December 2020 2, Nr. 4 (06.01.2021): 203–13. http://dx.doi.org/10.36548/jucct.2020.4.003.
Der volle Inhalt der QuelleYin, Peng-Yeng, Po-Yen Chen, Ying-Chieh Wei und Rong-Fuh Day. „Cyber Firefly Algorithm Based on Adaptive Memory Programming for Global Optimization“. Applied Sciences 10, Nr. 24 (15.12.2020): 8961. http://dx.doi.org/10.3390/app10248961.
Der volle Inhalt der QuelleKozik, Rafał, und Michał Choraś. „Pattern Extraction Algorithm for NetFlow-Based Botnet Activities Detection“. Security and Communication Networks 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6047053.
Der volle Inhalt der QuelleDo, ChoXuan, Nguyen Quang Dam und Nguyen Tung Lam. „Optimization of network traffic anomaly detection using machine learning“. International Journal of Electrical and Computer Engineering (IJECE) 11, Nr. 3 (01.06.2021): 2360. http://dx.doi.org/10.11591/ijece.v11i3.pp2360-2370.
Der volle Inhalt der QuelleMa, Wu Bin, Ming Xing Liu, Su Deng und Hong Bin Huang. „A Spatial Resource Top-K Query Algorithm in Cyber Physical System“. Advanced Materials Research 774-776 (September 2013): 1725–28. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1725.
Der volle Inhalt der QuelleDissertationen zum Thema "Cyber Algorithm"
Thames, John Lane. „Advancing cyber security with a semantic path merger packet classification algorithm“. Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/45872.
Der volle Inhalt der QuelleChatterjee, Aakriti. „Development of an RSA Algorithm using Reduced RISC V instruction Set“. University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617104502129937.
Der volle Inhalt der QuelleRoychowdhury, Sayak. „Data-Driven Policies for Manufacturing Systems and Cyber Vulnerability Maintenance“. The Ohio State University, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=osu1493905616531091.
Der volle Inhalt der QuelleGuymon, Daniel Wade. „Cyber-physical Algorithms for Enhancing Collaboration“. Thesis, Virginia Tech, 2012. http://hdl.handle.net/10919/31919.
Der volle Inhalt der QuelleMaster of Science
Gujrati, Sumeet. „Models and algorithms for cyber-physical systems“. Diss., Kansas State University, 2013. http://hdl.handle.net/2097/16922.
Der volle Inhalt der QuelleDepartment of Computing and Information Sciences
Gurdip Singh
In this dissertation, we propose a cyber-physical system model, and based on this model, present algorithms for a set of distributed computing problems. Our model specifies a cyber-physical system as a combination of cyber-infrastructure, physical-infrastructure, and user behavior specification. The cyber-infrastructure is superimposed on the physical-infrastructure and continuously monitors its (physical-infrastructure's) changing state. Users operate in the physical-infrastructure and interact with the cyber-infrastructure using hand-held devices and sensors; and their behavior is specified in terms of actions they can perform (e.g., move, observe). While in traditional distributed systems, users interact solely via the underlying cyber-infrastructure, users in a cyber-physical system may interact directly with one another, access sensor data directly, and perform actions asynchronously with respect to the underlying cyber-infrastructure. These additional types of interactions have an impact on how distributed algorithms for cyber-physical systems are designed. We augment distributed mutual exclusion and predicate detection algorithms so that they can accommodate user behavior, interactions among them and the physical-infrastructure. The new algorithms have two components - one describing the behavior of the users in the physical-infrastructure and the other describing the algorithms in the cyber-infrastructure. Each combination of users' behavior and an algorithm in the cyber-infrastructure yields a different cyber-physical system algorithm. We have performed extensive simulation study of our algorithms using OMNeT++ simulation engine and Uppaal model checker. We also propose Cyber-Physical System Modeling Language (CPSML) to specify cyber-physical systems, and a centralized global state recording algorithm.
Furrer, Frank J., und Georg Püschel. „From Algorithmic Computing to Autonomic Computing“. Technische Universität Dresden, 2018. https://tud.qucosa.de/id/qucosa%3A30773.
Der volle Inhalt der QuelleKem, Oudom. „Modélisation et exploitation des connaissances de l’environnement : une approche multi-agents pour la recherche d’itinéraires multi-objectifs dans des environnements ubiquitaires“. Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEM023.
Der volle Inhalt der QuelleFrom intelligent artificial personal assistants to smart cities, we are experiencing the shifting towards Internet of Things (IoT), ubiquitous computing, and artificial intelligence. Cyber-physical entities are embedded in social environments of various scales from smart homes, to smart airports, to smart cities, and the list continues.This paradigm shift coupled with ceaseless expansion of the Web supplies us with tremendous amount of useful information and services, which creates opportunities for classical problems to be addressed in new, different, and potentially more efficient manners. Along with the new possibilities, we are, at the same time, presented with new constraints, problems, and challenges. Multi-goal pathfinding, a variant of the classical pathfinding, is a problem of finding a path between a start and a destination which also allows a set of goals to be satisfied along the path. The aim of this dissertation is to propose a solution to solve multi-goal pathfinding in ubiquitous environments such as smart transits. In our solution, to provide an abstraction of the environment, we proposed a knowledge model based on the semantic web technologies to describe a ubiquitous environment integrating its cybernetic, physical, and social dimensions. To perform the search, we developed a multi-agent algorithm based on a collaborative and incremental search algorithm that exploits the knowledge of the environment to find the optimal path. The proposed algorithm continuously adapts the path to take into account the dynamics of the environment
Staub, Nicolas. „Models, algorithms and architectures for cooperative manipulation with aerial and ground robots“. Thesis, Toulouse 3, 2018. http://www.theses.fr/2018TOU30169/document.
Der volle Inhalt der QuelleIn recent years, the subject of physical interaction for aerial robots has been a popular research area with many new mechanical designs and control approaches being proposed. The aerial robotics community is currently observing a paradigm shift from classic guidance, navigation, and control tasks towards more unusual tasks, for example requesting aerial robots to physically interact with the environment, thus extending the manipulation task from the ground into the air. This thesis contributes to the field of aerial manipulation by proposing a novel concept known has Multiple Aerial-Ground Manipulator System or MAGMaS, including what appears to be the first experimental demonstration of a MAGMaS and opening a new route of research. The motivation behind associating ground and aerial robots for cooperative manipulation is to leverage their respective particularities, ground robots bring strength while aerial robots widen the workspace of the system. The first contribution of this work introduces a meticulous system model for MAGMaS. The system model's properties and potential extensions are discussed in this work. The planning, estimation and control methods which are necessary to exploit MAGMaS in a cooperative manipulation tasks are derived. This works proposes an optimal control allocation scheme to exploit the MAGMaS redundancies and a general model-based force estimation method is presented. All of the proposed techniques reported in this thesis are integrated in a global architecture used for simulations and experimental validation. This architecture is extended by the addition of a tele-presence framework to allow remote operations of MAGMaS. The global architecture is validated by robust demonstrations of bar lifting, an application that gives an outlook of the prospective use of the proposed concept of MAGMaS. Another contribution in the development of MAGMaS consists of an exploratory study on the flexibility of manipulated loads. A vibration model is derived and exploited to showcase vibration properties in terms of control. The last contribution of this thesis consists of an exploratory study on the use of elastic joints in aerial robots, endowing these systems with mechanical compliance and energy storage capabilities. Theoretical groundings are associated with a nonlinear controller synthesis. The proposed approach is validated by experimental work which relies on the integration of a lightweight variable stiffness actuator on an aerial robot
Markwood, Ian. „Offensive and Defensive Security for Everyday Computer Systems“. Scholar Commons, 2018. https://scholarcommons.usf.edu/etd/7336.
Der volle Inhalt der QuelleRukavina, Ivan. „Cyber-physics intrinsic modelling for smart systems“. Thesis, Compiègne, 2021. http://bibliotheque.utc.fr/EXPLOITATION/doc/IFD/2021COMP2581.
Der volle Inhalt der QuelleIn this thesis, a multi-scale and multi-physics coupling computation procedure for a 2D and 3D setting is presented. When modeling the behavior of a structure by a multi-scale method, the macro-scale is used to describe the homogenized response of the structure, and the micro-scale to describe the details of the behavior on the smaller scale of the material where some inelastic mechanisms, like damage or plasticity, can be taken into account. The micro-scale mesh is defined for each macro-scale element in a way to fit entirely inside it. The two scales are coupled by imposing a constraint on the displacement field over their interface. The computation is performed using the operator split solution procedure on both scales, using the standard finite element method. In a 2D setting, an embedded discontinuity is implemented in the Q4 macroscale element to capture the softening behavior happening on the micro-scale. For the micro-scale element, a constant strain triangle (CST) is used. In a 3D setting, a macro-scale tetrahedral and hexahedral elements are developed, while on the micro-scale Timoshenko beam finite elements are used. This multi-scale methodology is extended with a multi-physics functionality, to simulate the behavior of a piezoelectric material. An additional degree of freedom (voltage) is added on the nodes of the 3D macro-scale tetrahedral and hexahedral elements. For the micro-scale element, a Timoshenko beam element with added polarization switching model is used. Also, a multi-scale Hellinger- Reissner formulation for electrostatics has been developed and implemented for a simple electrostatic patch test. For implementing the proposed procedure, Finite Element Analysis Program (FEAP) is used. To simulate the behavior on both macro and micro-scale, FEAP is modified and two different version of FEAP code are implemented – macroFEAP and microFEAP. For coupling, the two codes are exchanging information between them, and Component Template Library (CTL) is used. The capabilities of the proposed multi-scale approach in a 2D and 3D pure mechanics settings, but also multi-physics environment have been shown. The theoretical formulation and algorithmic implementation are described, and the advantages of the multi-scale approach for modeling heterogeneous materials are shown on several numerical examples
Bücher zum Thema "Cyber Algorithm"
Analog - A Cyber-Dystopian Noir Vol. 1: Death by Algorithm. Image Comics, 2018.
Den vollen Inhalt der Quelle findenSheng, Quan Z., und Brij B. Gupta. Machine Learning for Computer and Cyber Security: Principle, Algorithms, and Practices. Taylor & Francis Group, 2019.
Den vollen Inhalt der Quelle findenSheng, Quan Z., und Brij B. Gupta. Machine Learning for Computer and Cyber Security: Principle, Algorithms, and Practices. Taylor & Francis Group, 2019.
Den vollen Inhalt der Quelle findenSheng, Quan Z., und Brij B. Gupta. Machine Learning for Computer and Cyber Security: Principle, Algorithms, and Practices. Taylor & Francis Group, 2019.
Den vollen Inhalt der Quelle findenSheng, Quan Z., und Brij B. Gupta. Machine Learning for Computer and Cyber Security: Principle, Algorithms, and Practices. Taylor & Francis Group, 2019.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Cyber Algorithm"
Hong, Sung-Soo, und Sang-Kil Kim. „Mobile Animation Algorithm for Cyber Museum“. In Lecture Notes in Computer Science, 586–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44864-0_61.
Der volle Inhalt der QuelleNguyen, Viet T., Alla G. Kravets und Tu Q. H. Duong. „Predicting Research Trend Based on Bibliometric Analysis and Paper Ranking Algorithm“. In Cyber-Physical Systems, 109–23. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67892-0_10.
Der volle Inhalt der QuellePolyakov, Vladimir, Dmitriy Buhanov, Maxim Panchenko, Margarita Redkina und Sergey Chernikov. „Research of the ELA Algorithm for Identifying Editing Fact in Jpeg Images“. In Cyber-Physical Systems, 249–58. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-67892-0_21.
Der volle Inhalt der QuelleChen, Yun, Yunlan Du und Xiaomei Cao. „Density Peak Clustering Algorithm Based on Differential Privacy Preserving“. In Science of Cyber Security, 20–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34637-9_2.
Der volle Inhalt der QuelleBaruch, Moran, und Gil David. „Domain Generation Algorithm Detection Using Machine Learning Methods“. In Cyber Security: Power and Technology, 133–61. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75307-2_9.
Der volle Inhalt der QuelleWang, Jing, Fuqi Song, Aihua Yin und Hui Chen. „Firefly Algorithm Based on Dynamic Step Change Strategy“. In Machine Learning for Cyber Security, 347–55. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62460-6_31.
Der volle Inhalt der QuelleCheng, Xianfu, Yanqing Yao und Ao Liu. „An Improved Privacy-Preserving Stochastic Gradient Descent Algorithm“. In Machine Learning for Cyber Security, 340–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62223-7_29.
Der volle Inhalt der QuelleAlsuwat, Emad, Hatim Alsuwat, Marco Valtorta und Csilla Farkas. „Cyber Attacks Against the PC Learning Algorithm“. In ECML PKDD 2018 Workshops, 159–76. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-13453-2_13.
Der volle Inhalt der QuellePeng, Jiao, und Shu Gong. „Optimization of Collaborative Filtering Algorithm in Movie Recommendation System“. In Machine Learning for Cyber Security, 11–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62463-7_2.
Der volle Inhalt der QuelleLiu, Bo, Jianhou Gan, Jun Wang und Bin Wen. „Product Consistency Joint Detection Algorithm Based on Deep Learning“. In Machine Learning for Cyber Security, 297–311. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62463-7_28.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Cyber Algorithm"
Fisher, Ashwin, Rusty Baldwin, James T. Graham und Ronald Riley. „Block-level algorithm classification based on RF side-channel“. In Cyber Sensing 2018, herausgegeben von Peter Chin und Igor V. Ternovskiy. SPIE, 2018. http://dx.doi.org/10.1117/12.2303847.
Der volle Inhalt der QuelleRiley, Ronald A., James T. Graham, Ashwin Fisher, Rusty O. Baldwin und Ryan M. Fuller. „Generalization of algorithm recognition in RF side channels between devices“. In Cyber Sensing 2018, herausgegeben von Peter Chin und Igor V. Ternovskiy. SPIE, 2018. http://dx.doi.org/10.1117/12.2304468.
Der volle Inhalt der QuelleDong, Huailin, Mingyuan He und Ming Qiu. „Optimized Gray-Scale Image Watermarking Algorithm Based on DWT-DCT-SVD and Chaotic Firefly Algorithm“. In 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 2015. http://dx.doi.org/10.1109/cyberc.2015.15.
Der volle Inhalt der QuelleChang, Jian, Bin Li, Guowei Zhang, Zhida Liang und Cong Wang. „The Control Algorithm of 7 DOF Manipulator Based on Hybrid Force and Position Algorithm“. In 2017 IEEE 7th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2017. http://dx.doi.org/10.1109/cyber.2017.8446183.
Der volle Inhalt der QuelleChen, Min, XueDong Gao und HuiFei Li. „An efficient parallel FP-Growth algorithm“. In 2009 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 2009. http://dx.doi.org/10.1109/cyberc.2009.5342148.
Der volle Inhalt der QuelleSun, Zhixin, Yadang Chen und Zhixin Sun. „An Algorithm Based on Directed Graph“. In 2010 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 2010. http://dx.doi.org/10.1109/cyberc.2010.63.
Der volle Inhalt der QuelleO'Brien, Neil S., Steven J. Johnston, Elizabeth E. Hart, Kamal Djidjeli und Simon J. Cox. „Exploiting Cloud Computing for Algorithm Development“. In 2011 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 2011. http://dx.doi.org/10.1109/cyberc.2011.60.
Der volle Inhalt der QuelleChen, Liang, Zhang Tong, Wen Liu und Chengmin Gao. „Non-interactive Exponential Homomorphic Encryption Algorithm“. In 2012 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 2012. http://dx.doi.org/10.1109/cyberc.2012.44.
Der volle Inhalt der QuelleJones, Keith J., und Yong Wang. „An Optimized Running Window Entropy Algorithm“. In 2018 National Cyber Summit (NCS). IEEE, 2018. http://dx.doi.org/10.1109/ncs.2018.00016.
Der volle Inhalt der QuelleBelaidi, Abderrahmane, und Hadjira Belaidi. „Optimization algorithm of manipulator robot performances“. In 2014 IEEE 4th Annual International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER). IEEE, 2014. http://dx.doi.org/10.1109/cyber.2014.6917459.
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