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Abstract: This research focuses special attention on a new class of worms called Camouflaging worm (C-Worm). The key difference between C-Worm and traditional worms is that, it displays the ability to intelligently analyse and make changes to its scan traffic volume over time. This new class of active worms is an attack that spread itself on the internet by exploiting vulnerabilities on computer systems.
Keywords: C-Worm, Camouflaging, traditional, vulnerabilities.
References:
1. Cheetancheri, S. G., Agosta, J. M., Dash, D. H., Levitt, K. N., Rowe, J., & Schooler, E. M. (2006). A distributed host-based worm detection system. LSAD '06 Proceedings of the 2006 SIGCOMM workshop on Large-scale attack defense (pp. 107-113). ACM.
2. Chen, Z., Gao, L., & Ji, C. (2003). On Effectiveness of defense systems against active worms.
3. Gu, G., Sharif, M., Qin, X., Dagon, D., Lee, W., & Riley, G. (2004). Worm detection, early warning and response based on local victim information. 20 th Annual Computer Security Applications Conference.
4. M.A.BASEER, M., NARAYANA, M. P., & LAHANE, M. S. (2013). Modeling and Detection of Camouflaging Worm. INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGIES, VOL. 01, ISSUE 02, 99-104. Retrieved 12 2013
5. M.K, P. C., & P.V, M. (2012). Modelling and Detection of Camouflaging Worms. International Journal of Emerging Technology and Advanced Engineering, 2-3. Retrieved 12 2013
6. Madhusudan, B., & Lockwood, J. (2004). Design of a System for Real-Time Worm Detection. HOTI 12.
7. Pele Li Mehdi Salour, X. S. (2008). Survey of Internet Worm Detection and Containment. IEEE Communications Surveys.
8. Saudi, M. M., Seman, K., Tamil, E. M., Yamani, M., Idris, I., & Visualization, A. (2008). Worm Analysis through Computer Simulation (WAtCoS). Proceedings of the World Congress in Engineering 2008, 1.
9. Singh, S., Estan, C., Varghese, G., & Savage, S. (2003). The EarlyBird System for Real-time Detection of Unknown Worms. Technical Report CS2003-0761. University of California, San Diego.
10. Talli, P., & Krishna, M. V. (2012). Detection of Active Internet Worm: Camouflaging Worm. nternational Journal of Electronics Communication and Computer Engineering, 3(5), 1172-1175. Retrieved 12 2013
11. Venkataraman, S., Song, D., Gibbons, P. B., & Blum, A. (2004). New Streaming Algorithms for Fast Detection of Superspreaders. Intel Corporation. Retrieved 1 2014
12. Wang, X., Yu, W., Champion, A., Fu, X., & Xuan, D. (2007). Detecting Worms via Mining Dynamic Program Execution. Third International Conference on Security and
Privacy in Communication Networks and the Workshops, SecureComm 2007, (pp. 412-421). Nice,France.
13. Wu, J., Vangala, S., Gao, L., & Kwiat, K. (2004). An Effective Architecture and Algorithm for Detecting Worms with Various Scan Techniques. 11th Annual Network and Distributed System Security Symposium (NDSS '04).
14. Yi, B. k. (2006). Digital Signatures.
15. Yu, W., Wang, X., Calyam, P., Xuan, D., & Zhao, W. (2011). Modeling and Detection of Camouflaging Worm. IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING, VOL., 1-3. Retrieved 12 29, 2013
16. Yu, W., Wang, X., PrasadCalyam, Xuan, D., & Zhao, W. (n.d.). On Detecting Camouflaging Worm.
17. Yu, W., Wang, X., Xuan, D., & Lee, D. (2006). Effective Detection of Active Worms with Varying Scan Rate. IEEE International Conference on Security and Privacy in Communication Networks (SecureComm).
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Abstract: This paper deals with the influence of wind turbine generators on oscillatory stability. Examining low-frequency oscillatory stability is important for understanding the weakest mode that leads to oscillatory problems. The performance of three types of wind generators, the doubly fed induction generator, the squirrel cage induction generator, and the permanent magnet synchronous generator, when connected to a grid was examined. Eigenvalue analysis and time-domain analysis helped to identify oscillatory instability problems in the wind farm. Moreover, the advantages of a high voltage direct current link (HVDC) for improving instability issues was studied. The simulation results show the significance of the DC link in improving the stability of the system by isolating the wind farm at the point of common couple from a fault event. The simulation software tool DIgSILENT PowerFactory was used for all modelling of wind farms, HVDC, and the IEEE 14-bus test system.
Keywords: Wind Turbine Generators, Oscilatory Stability, DFIG, PMSG, SCIG, HVDC, IEEE 14-bus test system, Eigenvalue analysis, DIgSILENT PowerFactory.
References:
1. K. R. Steve Sawyer, "Global Wind Report," belgium 2010.
2. K. R. Steve Sawyer, "Global Wind energy Outlook 2010," belgium 2010.
3. S. Heier, Grid Integration of Wind Energy Conversion System, 1998.
4. D. Thakur and N. Mithulananthan, "Influence of Constant Speed Wind Turbine Generator on Power System Oscillation," Electric Power Components and Systems, vol. 37, pp. 478-494, 2009.
5. G. Rogers, power system Oscillations: Kluwer Academic Publishers, 2000.
6. P. W. Sauer, Power Systen Dynamics and Stability: Prentice-Hall, Inc., 1998.
7. P. Kundur, Power System Stability and Control. New York: McGraw-Hill, Inc., 1994.
8. O. A. Almasari, "Low Frequency Oscillatory Stability Study of Power System with Wind Farms," Master Degree, School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane, 2011.
9. W. Chen, S. Libao, W. Liming, and N. Yixin, "Small signal stability analysis considering grid-connected wind farms of DFIG type," in Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE, 2008, pp. 1-6.
10. A. F. Snyder, "Inter-Area Oscillation Damping with Power System Stabilizers and synchronized Phasor Measurements," Master thesis, Electrical Engineering, Virginia Polytechnic Institute and State University, 1997.
11. F. J. Swift and H. F. Wang, "The connection between modal analysis and electric torque analysis in studying the oscillation stability of multi-machine power systems," International Journal of Electrical Power & Energy Systems, vol. 19, pp. 321-330, Jun 1997.
12. J. G. Slootweg and W. L. Kling, "The impact of large scale wind power generation on power system oscillations," Electric Power Systems Research, vol. 67, pp. 9-20, Oct 2003.
13. R. C. Burchett and G. T. Heydt, "Probabilistic Methods For Power System Dynamic Stability Studies," Power Apparatus and Systems, IEEE Transactions on, vol. PAS-97, pp. 695-702, 1978.
14. R. D. Fernández, R. J. Mantz, and P. E. Battaiotto, "Impact of wind farms on a power system. An eigenvalue analysis approach," Renewable Energy, vol. 32, pp. 1676-1688, 2007.
15. D. Gautam, V. Vittal, and T. Harbour, "Impact of increased penetration of DFIG based wind turbine generators on transient and small signal stability of power systems," in Power and Energy Society General Meeting, 2010 IEEE, 2010, pp. 1-1.
16. Y. Sun, L. Wang, G. Li, and J. Lin, "A review on analysis and control of small signal stability of power systems with large scale integration of wind power," in Power System Technology (POWERCON), 2010 International Conference on, 2010, pp. 1-6.
17. D. Devaraj and R. Jeevajyothi, "Impact of wind turbine systems on power system voltage stability," in Computer, Communication and Electrical Technology (ICCCET), 2011 International Conference on, 2011, pp. 411-416.
18. G. Michalke and A. D. Hansen, "Modelling and control of variable speed wind turbines for power system studies," Wind Energy, vol. 13, pp. 307-322, May 2010.
19. I. D. Margaris and N. D. Hatziargyriou, "Direct drive synchronous generator wind turbine models for power system studies," in Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2010), 7th Mediterranean Conference and Exhibition on, 2010, pp. 1-7.
20. Kodsi S. K. M, Canizares C. “Modeling and Simulation of IEEE 14 Bus System with FACTS controllers”, Technical report 2003-3, University of Waterloo, On, Canada, 2003.
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Abstract: This paper introduces Internet of Things (IoTs), which offers capabilities to identify, share information and connect worldwide physical objects into a single system. In this research work, we will discuss how we can connect various objects in a better way and what are the basic requirements and functions to make them communicate and consequently make this world unified. This survey summarizes the easiest and most feasible way of connecting various objects of IoT and it also discuss about a prototype for smart parking area application of IoT. In this prototype we have used a microcontroller to handle all the devices. Security is also added to this model using scanners and finger print detectors.
Keywords: Internet of Things (IoT), Parking, Security, Addressability.
References:
1. C. Perera, A. Zaslavsky, P. Christen, and D. Georgakopoulos, “Context Aware Computing for The Internet of Things: A Survey” IEEE Communications Surveys & Tutorials, 2013, pp. 1-41.
2. M. Palattella, N. Accettura, X. Vilajosana, T. Watteyne, L. Grieco, G. Boggia, and M. Dohler, "Standardized protocol stack for the internet of (important) things," Proceedings of IEEE, 2012, pp. 1-18.
3. O. Vermesan, P. Friess,and A. Furness, “The Internet of Things New Horizons” IERC - Internet of Things European Research Cluster, 3rd edition of the Cluster Book, ISBN hard cover: 978 - 0 - 9553707 - 9 – 3,2012.
4. Bari, N. ; Mani, G. ; Berkovich, S., “Internet of Things as a Methodological Concept”, IEEE, 2013, pp. 48 – 55.
5. Yinghui Huang ; Guanyu Li, “Descriptive models for Internet of Things”, IEEE International Conference on Intelligent Control and Information Processing (ICICIP), 2010, pp. 483 – 486.
6. Coetzee, L. ; Eksteen, J., “The Internet of Things - promise for the future? An introduction”, IEEE IST-Africa Conference Proceedings, 2011, pp. 1 – 9.
7. Zhu Yu ; Wang Tie-Ning, “Research on the Visualization of Equipment Support Based on the Technology of Internet of Things”, IEEE Second International Conference on Instrumentation, Measurement, Computer, Communication and Control IMCCC '12 Proceedings, 2012, pp. 1352 – 1357.
8. Yinghui Huang ; Guanyu Li, “A Semantic Analysis for Internet of Things”, IEEE International Conference on Intelligent Computation Technology and Automation (ICICTA), 2010, pp. 336 – 339.
9. Lu Tan ; Comput.; Neng Wang, “Future internet: The Internet of Things”, Advanced Computer Theory and Engineering (ICACTE), 2010 3rd International Conference on IEEE, 2010, pp. V5-376 - V5-380.
10. Mingfang Du ; Jianjun Fang ; Haiqing Cao, “A new solution for city parking guiding based on Internet of Things and multi-level multi-agent”, IEEE, 2011, pp. 4093 – 4096.
11. Yanlin Yin ;Dalin Jiang, “Research and Application on Intelligent Parking Solution Based on Internet of Things”, IEEE, 2013, pp. 101 – 105.
12. Tsung-hua Hsu, Jing-Fu Liu, Pen-Ning Yu, Wang-Shuan Lee and Jia-Sing Hsu, “Development of an automatic parking system for vehicle”, IEEE, 2008, pp. 1-6.
13. Daobin Wang ; Huawei Liang ; Tao Mei ; Hui Zhu, “Research on self-parking path planning algorithms”, IEEE, 2011, pp. 258 – 262.
14. Oentaryo, R.J. ,Pasquier, M., “Self-trained automated parking system”, IEEE, 2004, vol. 2; pp. 1005 – 1010.
15. M. Nizam Kamarudin, “Development of an automatic parallel parking system for nonholonomic mobile robot”, IEEE International Conference on, Electrical, Control and Computer Engineering (INECCE) 2011, pp. 45-49.
16. Tang, V.W.S. ,Yuan Zheng ; Jiannong Cao, “An Intelligent Car Park Management System based on Wireless Sensor Networks”, IEEE 1st International Symposium on Pervasive Computing and Applications, 2006, pp. 65 – 70.
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