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example, changes and even slight divergence form the basic part of the flow can be revealed by it. sponsored by Natural Science Foundation of Hubei Province under Grant No. 2008CDA021. REFERENCES Fig.3 Case 2 for ICMP L3retriever Ping flow: (a) Original alarm flow series (dotted line) and SSA-reconstructed series (continuous line) corresponding to normal flow behavior. (b) Residual series defined as the difference between the original and reconstructed series. IV. CONCLUSIONS Network threat identification and evaluation aims to extract knowledge of current security threat and status from raw security data. In practice, identifying the security incidents of true threat from IDS alarms is really difficult because the amount of alarms is usually overwhelming and there are a lot of redundant and false alarms. In this paper we focus processing aggregated alarm flow in real-time. Using the SSA, we first explore the intrinsic dimensionality and structure of the timeseries corresponding to alarm flow. Then we capture the leading components, which represent the main part of the flow, to make threat evaluation. Reconstructed signals enable the administrator to have a real-time view of security threat situation. In addition, the threat evaluation can work well in dynamic and non-stationary network environment. Based on the approach, we do not need to know the parametric model of the considered time series and have the ability to autonomously adapt to shifts in the structure of the alarm flow. We believe that the method could be used as such, or in complement to other means of correlation, to monitor alarms. In the next work, we plan to focus on automatically detecting changes in alarm flow and further improve our threat evaluation approach. ACKNOWLEDGMENT This work is supported by the National Natural Science Foundation of China under Grant No. 60573120, and by the National High Technology Research and Development Program of China (863 Program) under Grant No. 2007AA01Z420, and by the key project [1] J. Allen, A. Christie, et al., (2007) State of the Practice of Intrusion Detection Technologies. Available via Software Engineering Institute. http://www.sei.cmu.edu/publications/documents/99.reports /99tr028/99tr028abstract.html. [2] P. Ning, Y. Cui, and D. S. Reeves: Constructing attack scenarios through correlation of intrusion alerts. In Proceedings of the 9th ACM Conference on Computer and Communications Security, Nov 18-22 2002, Washington, DC, United States, 2002. [3] P. Ning, Y. Cui, D. S. Reeves, and D. Xu: Techniques and tools for analyzing intrusion alerts. In: ACM Transactions on Information and System Security, vol. 7, pp.274, 2004. [4] W. Lee, S. J. Stolfo, A Framework for Constructing Features and Models for Intrusion Detection Systems, ACM Transactions on Information and System Security 3(4) (2000) 227–261 [5] X. Qin and W. Lee. Statistical Causality Analysis of INFOSEC Alert Data. In Proc. of the RAID’03, Springer– Verlag, 2003. [6] F. Cuppens.: Managing alerts in multi-intrusion detection environment. In: Proceedings 17th annual computer security applications conference. New Orleans; 2001. [7] F. Cuppens, A. Miege: Alert correlation in a cooperative intrusion detection framework. In: Proceedings of the 2002 IEEE symposium on security and privacy; 2002. [8] K. Julisch and M. Dacier. Mining Intrusion Detection Alarms for Actionable Knowledge. In Proc. of the SIGKDD’02, 2002. [9] C. Kruegel and W. Robertson. Alert verification: Determining the success of intrusion attempts. In Proc. of the DIMVA’06, Dortmund, Germany, July 2006. [10] P. Ammann, D. Wijesekera, and S. Kaushik, Scalable, Graph-Based Network Vulnerability Analysis, Proceedings of the 9th ACM Conference on Computer and Communications Security, New York: ACM Press, 2002, 217–224 [11] R. Ritchey and P. Amman, Using Model Checking to Analyze Network Vulnerabilities, Proceedings of the 2000 IEEE Symposium on Security and Privacy, pp. 156-165, 2000 [12] A. Gehani, G. Kedem Rheostat: Real-time Risk Management. In Proceedings of the 7th International Symposium on Recent Advances in Intrusion Detection, 2004. [13] A. Arnes, F. Valeur, G.. Vigna., R. A. Kemmerer,Using Hidden Markov Models to Evaluate the Risk of Intrusions. in: Proceedings of the International Symposium on the Recent Advances in Intrusion Detection(RAID 2006): Springer-Verlag, 2006. 145-164. [14] N. Nekrutkin, V. Zhigljavsky, 2001. Analysis of Time Series Structure—SSA and Related Techniques. Chapman & Hall/CRC, Boca Raton, FL, pp. 13-78. [15] Vautard, R., Yiou, P., Ghil, M. Singular-spectrum analysis : a toolkit for short, noisy chaotic signals. Physica D, vol. 58, pp. 95-126, 1992. [16] B. Caswell, M. Roesch (2004) Snort: The open source network intrusion detection system. Available via Snort. http://www.snort.org/ 96
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. 097-100 Fuzzy Control of Intelligent Vehicle Based on Visual Navigation System Tingjian Zhong 1 , and Meilian Qiu 2 1 Jiangxi Vocational & Technical College of Electricity, Nanchang, China Email:jxdlztj@163.com 2 Nanchang Power Supply Company, Nanchang, China Email:qiuqiu6872@163.com Abstract—The research on intelligent vehicle mainly includes safety monitoring, intelligent anticollision, aided driving, auto driving, behavior planning decision-making, system structure, and synthetical integration, etc. Sensor and control algorithm are major factors influencing the development of intelligent vehicle. This paper introduces an intelligent vehicle system with chip of FRSSCALE MC9SDG128. By adding fuzzy algorithm into this research on turning control angle of intelligent vehicle, it makes decision according to the lateral error and orientation error. This system, which includes automatic recognition and finished special function, is simple and useful, lower requirement to hardware and has capability of adapting existing structured road environment. these following main factors should be considered: first, hardware requires good reliability; second, use real-time processing of visual-guided high-speed method to lead the camera image for information collection; In addition, it should determine the performance of the sensor correctly to meet the requirements of intelligent vehicle functions. Index Terms—Intelligent vehicle, path tracking, fuzzy control I. INTRODUCTION Intelligent vehicle, which is also called wheel Mobil Robot, is a synthetical system [1][2] that contains environmental perception, planning and decision-making, auto-driving and other functions. Intelligent vehicle involves many fields, such as computer science, communications, artificial intelligence, signal processing, pattern recognition, control theory and so on. Intelligent vehicle has a wide range of applications prospect in areas such as the military, civil and scientific research. It has attracted the attention of large companies and governments. From the mid-and late eighties last century, the world’s major developed countries have launched a series of research and have effective development on intelligent vehicle. Road detection technology is not only as an important key technologies, but also as an important indication of the level in intelligent vehicle visual navigation system. This paper focuses on intelligent vehicle control system in terms of speed, looking for line control. Based on the traditional idea of fuzzy control, in accordance with the specific requirements of the process of the intelligent vehicle speed, line regulation, and speed of the actual situation, a fuzzy parameter self-tuning fuzzy control method is introduced. Experimental results show that this method is suitable for intelligent vehicle for speed and line-conditioning. II. INTELLIGENT VEHICLE DESIGN PROGRAM Hardware design of controller not only affects the overall performance of the intelligent vehicle, but also related to the manufacturing cost. While choose hardware, Figure 1. framework of intelligent vehicle haredware system Intelligent vehicle contains six modules: control processor MC9S12DG128, servo drive module, motor driver module, image acquisition module, speed acquisition module and auxiliary debugging module. Where, S12 singlechip is the hardcore of this system. It is responsible for receiving image data of path and some information such as speed feedback, and dealing with the information correctly into the control volume for controlling the drive motor and steering gear. Steering gear module and drive module, respectively, response for steering and driving of this vehicle model. image acquisition module is composed of S12 AD module, the chip LM1881 chip, peripheral circuits and camera. Its function is to obtain the image data of the path in front for further analysis of S12. Speed acquisition module, which is composed of the reflective photoelectric sensor and black-and-white ribbons attached to the driven gear, detect the cumulative number of pulse reflection models to get the speed value. Auxiliary debugging module is for writing program of vehicle model, debugging and testing functions, as well as the settings for state control of intelligent vehicle, system parameters and operation of policy and so on. The hardware system diagram of this vehicle structure is shown in Fig.1. © 2010 ACADEMY PUBLISHER AP-PROC-CS-10CN006 97
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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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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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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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[3] Y. Li, S. M. Chung, and J. D. H
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some drilling fluid produces hydrog
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ACKNOWLEDGMENT This work is funded
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[4] Huang, Hung, and J. Y. jen Hsu.
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ontology, and the domain dictionary
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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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[2] Kumaravel, N., and Kavitha, V.,
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output, so BP network has been wide
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Architecture (AMBA) a new bus archi
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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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to execution time. Also, DBA would
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Liping Chen .......................
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