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31 30 29 28 27 theoretical analysis of coordinated motion and obstacleeluding aggregating stability of the swarm systems, the convex polygon formation behavior control and obstacleeluding aggregating behavior for multi-agent system is analyzed and discussed at last. For further work, the experiments will be conducted in the presence of dynamic obstacles … etc. Therefore, it is obviously the swarm aggregating results obtained from the isotropic range limited-perceive swarm model which has a definite reference value in the multi-agent coordination and control literature. 26 31 32 33 34 35 36 Figure 10. The communication and coverage search capabilities showing for 4 agents members in the swarm to form the ideal diamond formation in plane at the time of entire positions In above graphs, black “*” represent original position, read “。” represent final position, blue “.” represent convergent trajectories of individuals, the polygonal vertex shows the final numerical imitation configuration position of UAV/robots. Considering the convenience of simulation, let ∇ yσ ( y) = 0 . In the figures of the relations between sides and angles of the desired formation configuration, black spheres represent final configuration position, V i , i = 1, L, M. represent different vehicles in the swarm systems. As mentioned above, the particular predefined convex polygon geometrical configuration formations were discussed. The simulation results in this section reify our theories of formation stabilization and for multi-agent systems. IV. CONCLUSIONS REMARKS This paper studied the coordinated motion control, obstacle avoidance and formation behaviour control problem of a group of isotropic range limited-perceive agents with interaction force between individuals moving in an n-dimensional Euclidean space. To solve the problem, based on the inspiration from biology, we proposed an isotropic range limited-perceive swarm model of swarm aggregating behavior for multi-agent system. Meanwhile, the model is a kinematic model. It is fit for individuals in which move basing on the Newton’s law in an environment can capture the basic convergence properties of biological populations in nature. Therefore, the final behavior of the swarms described by the model may be in harmony with real biological swarms well. Numerical simulation agrees very well with the ACKNOWLEDGMENT This work was supported by the National Natural Science Foundation of China (Grant No. 60975074). REFERENCES [1] C. M. Breder, “Equations descriptive of fish schools and other animal aggregations,” Ecology, Washington, vol. 35, no. 3, pp. 361–370, April 1954. [2] V. Gazi, K. M. Passino, “Stability analysis of swarms,” IEEE Transactions on Automatic Control, New York, vol. 48, no. 4, pp.692–697, April 2003. [3] S. Chen, H. Fang, “Modeling and stability analysis of large-scale intelligent swarm,” Control and Decision, Shenyang, vol. 20, no. 5, pp.490–494, May 2005. [4] Z. Xue, and J. Zeng, “Simulation Modeling and Analysis of Dynamics of Range Limit Perceived Group,” Journal of System Simulation, Beijing, vol.21, no.20, pp.6352-6355, October 2009. [5] J. G. Walker, “The Geometry of Satellite Clusters,”Journal Orbital Dynamics, London, vol. 35, pp. 345-354, Aug. 1982. [6] C. M. Saaj, V. Lappas, and V. Gazi, “Spacecraft Swarm Navigation and Control Using Artificial Potential Field and Sliding Mode Control,” IEEE International Conference on Industrial Technology, Mumbai, India, pp. 2646-2652, December 2006. [7] Y. Q. Chen, and Z. M. Wang “Formation control: a review and a new consideration,” 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems. (IROS 2005), Alberta, Canada, pp. 3181- 3186, 2-6 August 2005. [8] Y. S. Hanay, H. V. Hünerli, M. İ. Köksal, A. T. Şamiloğlu, and V. Gazi, “Formation Control with Potential Functions and Newton's Iteration,” Proceeding of the European Control Conference, Kos, Greece, pp. 4584-4590, July 2007. [9] V. Gazi, B. Fidan, M.İ. Hanay, and Y.S. Köksal, “Aggregation, foraging and formation control of swarms with non-holonomic agents using potential functions and sliding mode techniques,” Turkish Journal of Electrical Engineering and Computer Sciences (ELEKTRİK), Ankara, vol. 15, no. 2, pp.149–168, July 2007. 88
ISBN 978-952-5726-09-1 (Print) Proceedings of the Second International Symposium on Networking and Network Security (ISNNS ’10) Jinggangshan, P. R. China, 2-4, April. 2010, pp. 089-092 An Anomaly Detection Method Based on Fuzzy C-means Clustering Algorithm Linquan Xie 1 , Ying Wang 1,2 , Liping Chen 2 , and Guangxue Yue 1,2,3 1 Jiangxi University of Science and Technology, Ganzhou, China 2 Jiaxing University, Jiaxing, China 3 Guangdong University of Business Studies ,GuangZhou, China Email: {lq_xie@163.com , wy363100506@sina.com} Abstract—Anomaly detection based on network flow is the basis of the monitoring and response application of anomaly, it is also the important content in the fields of network and security management. In this paper, the fuzzy C-means clustering (FCM) algorithm was applied to detect abnormality which based on network flow. For the problems of the FCM, for example, it needs to preset a number of clusters and initialize sensitively, and easily fall into local optimum, the paper introduced the method combined with the average information entropy, support vector machine and fuzzy genetic algorithm etc.. These hybrid algorithms can solve the mentioned problems and classify more accurately. Finally based on the current development and the discussion of the research, it summarized the trends of the network flow anomaly detection in the paper. Index Terms—network flow, anomaly detection, intrusion detection, anomaly analysis I. INTRODUCTION As the rapid expansion and the growing popularity of the Internet, more and more information has been transmitted and stored through the network. Cognizing and studying the behavioral characteristics of the Internet users has gradually attracted people's interest, and it also used to cognize, manage, optimize various kinds of the network resources, and is an important basis of the network planning and design. However, compared with the development of the network application types, the improvement of the network management technology lags behind the development of the application. How to provide a safe, reliable and efficient service environment for the vast number of the Internet users, it needs to be resolved to the network management. Network flow analysis comes into being for resolving these issues; we can indirectly get hold of the statistical behavior of the network by statistical analysis of the network flow. It can enhance the manager of the network and security to troubleshoot the network anomaly, maintaining the normal network and to ensure the network security. At present, for the network flow anomaly detection, there has conducted extensive research, but the detection accuracy has been far from desirable. Nevertheless, the anomaly detection plays an irreplaceable role in discovering unknown anomaly network intrusion detection and network failure detection, etc. II. METHODS OF THE ANOMALY DETECTION BASED ON THE NETWORK FLOW For the methods of the network flow anomaly detection, there has been summarized of the related research work in recent years, including the following methods: the research method based on the features/behavior, the anomaly detection based on the statistics, the method based on the machine learning and the method based on the data mining, etc.. We can find the latter three methods of anomaly detection construct models on normal behaviors, it compares with the normal model to detect anomalies, thus it can effectively find out the known and unknown attacks. With the continuous research of intrusion detection, people obtain plentiful and substantial results, at the same time, people come to realize the pervasive problems of intrusion detection, such as the rate of detection can not meet the requirements of the modern high-speed network communications, a higher rate of false alarm and missing report in intrusion detection system (IDS), the IDS lacks of active defense and the interaction is not enough among the other network security devices. To solve the above problems, the research on intrusion detection which is distributed, intelligent and comprehensive of development becomes a matter of course. III. ANOMALY DETECTION OF NETWORK FLOW BASED ON FUZZY C-MEANS CLUSTERING (FCM) ALGORITHM How to provide a safe, reliable and efficient service environment for the vast number of the Internet users, it needs to be resolved for the network management. With the rapid development of network technology and continuous improvement for invasion of technology, the ways of new attacks emerge in endlessly, in order to detect and defense the unknown attacks, the intelligent methods are the focus of intrusion detection and have been widely used, such as data mining, neural networks, support vector machine, intelligent agent and etc.. Cluster analysis is used to discover the hidden patterns in the instance data and used to detect the meaningful characteristics in intrusion. How to accurately determine the intruder or the intrusion is the research topics of anomaly intrusion, while there are many algorithms for application of anomaly detection, among them, fuzzy C-means clustering (FCM) algorithm becomes a hot © 2010 ACADEMY PUBLISHER AP-PROC-CS-10CN006 89
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Proceedings The Second Internationa
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Table of Contents Message from the
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Zuming Xiao, Zhan Guo, Bin Tan, and
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Message from the Symposium Chairs T
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Second International Symposium on N
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model that can deal with time serie
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training for the Wushu competition
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information, called weak uncertain
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Student side Student side Student s
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If we define element of student as
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QoS, each frame data is divided int
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for Imaging Two and Three Phase Flo
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A. Profiling&following control algo
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Research and Realization about Conv
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PDF document structure is a tree st
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module, communication module, apper
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After the comprehensive performance
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system testing can be seen that the
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III. AUTONOMIC RESOURCE ALLOCATION
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The QoE i (T i ) is the ith user’
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nodes will be formed one cluster, t
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Where n is the number of data point
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Keyboard event Application User mod
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SQL Azure will eventually include a
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The nodes in the suffix tree are dr
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[3] Y. Li, S. M. Chung, and J. D. H
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some drilling fluid produces hydrog
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"normalization", whose membership b
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and minimum structural elements in
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esults of the spatial transform par
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B. Research on protocol actions Com
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ACKNOWLEDGMENT This work is funded
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A. Problem Description In a small b
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[4] Huang, Hung, and J. Y. jen Hsu.
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Further by calculating, the followi
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TABLE II. THE CONCENTRATION BETWEEN
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private key that obtained by using
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ontology, and the domain dictionary
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Eq.4 is NP-hard and can be solved b
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Where: G i is the selection field o
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If there is X j in a generation, wh
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Theorem 2.4([10]). Let L 1 and L 2
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The relation between the lattice im
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Figure 2. Example of single-step de
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evocation and the fourth group stor
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focused mainly on rule-based forms
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Ⅵ. CONCLUSION Figure 6. The Flask
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EDCF in comparison with DCF, has so
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B. Simulation results and analysis
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in routing table. When search resou
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others through the selective emotio
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such as weather information, techno
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the opening window, a related page
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1) Process context storage areas. I
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V. CONCLUSION In this thesis, sCPU-
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main types of horizontal search env
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Enterprise Portal security provides
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() t = [ S () t S () t ] T N S ,...
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[2] Kumaravel, N., and Kavitha, V.,
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output, so BP network has been wide
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input to the artificial neural netw
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(2) In a process (tokens from outsi
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The reduction process consists of t
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all eight normal vectors are identi
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is B object , and the number of emp
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xi, j y' = εα ( (1 + tanh( )) −
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Error 8000 7000 6000 5000 4000 3000
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Architecture (AMBA) a new bus archi
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In order to ensure the smooth proce
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In this paper, we adopt two Sobel o
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four layers.The far right of the gr
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TABLE II. OPERATIONAL EMPLOYEE TABL
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A. Object-relational Type To audit
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to execution time. Also, DBA would
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Liping Chen .......................
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