Providing a New Smart Camera Architecture for Intrusion Detection in Wireless Visual Sensor Network
الموضوعات :Meisam Sharifi Sani 1 , Amid Khatibi 2
1 - Department of Computer Engineering, Tehran Branch, Islamic Azad University, Tehran, Iran
2 - Department of Computer Engineering, Bardsir Branch, Islamic Azad University, Kerman, Iran
الکلمات المفتاحية: intrusion detection, smart cameras, security, visual sensor network, cloud computing.,
ملخص المقالة :
The wireless Visual sensor network is a highly functional domain of high-potential network generations in unpredictable and dynamic environments that have been deployed from a large number of uniform or non-uniform groups within the desired area, cause the realization of large regulatory applications from the military and industrial domain to hospital and environment. Therefore, security is one of the most important challenges in these networks. In this research, a new method of routing smart cameras with the help of cloud computing technology has been provided. The framework in the cloud computing management layer increases security, routing, inter interaction, and other features required by wireless sensor networks. Systematic attacks are simulated by a series of standard data collected at the CTU University related to the Czech Republic with RapidMiner software. Finally, the accuracy of detection of attacks and error rates with the suggested NN-SVM algorithm, which is a combination of vector machines and neural networks, is provided in the smart cameras based on the visual wireless sensor networks in MATLAB software. The results show that different components of the proposed architecture meet the quality characteristics of visual wireless sensor networks. Detection of attacks in this method is in the range of 99.24% and 99.35% in the worst and best conditions, respectively.
[1] V. D. Kale, “Recent Research Trends in Cloud computing,” vol. 6, no. 2, pp. 406–409, 2013.
[2] A. Boukerche et al., “A new solution for the time-space localization problem in wireless sensor network using UAV,” in Proceedings of the third ACM international symposium on Design and analysis of intelligent vehicular networks and applications - DIVANet ’13, 2013, pp. 153–160, doi: 10.1145/2512921.2512937.
[3] W. Schriebl, T. Winkler, A. Starzacher, and B. Rinner, “A pervasive smart camera network architecture applied for multi-camera object classification,” in 2009 3rd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2009, 2009, doi: 10.1109/ICDSC.2009.5289377.
[4] B. Rinner, T. Winkler, M. Quaritsch, B. Rinner, W. Schriebl, and W. Wolf, The evolution from single to pervasive smart cameras Epigenetic regulation of stress induced drug tolerance View project VECTO-Vehicle Energy Consumption Calculation Tool View project THE EVOLUTION FROM SINGLE TO PERVASIVE SMART CAMERAS. 2008.
[5] P. Chen et al., “Citric: A low-bandwidth wireless camera network platform,” in 2008 2nd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2008, 2008, doi: 10.1109/ICDSC.2008.4635675.
[6] P. Saastamoinen, S. Huttunen, V. Takala, M. Heikkilä, and J. Heikkilä, “Scallop: An open peer-to-peer framework for distributed sensor networks,” in 2008 2nd ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC 2008, 2008, doi: 10.1109/ICDSC.2008.4635712.
[7] S. A. M. Sharif and V. Jeoti, “Video wireless sensor network: Co-operative vision based localization method,” in Proceedings - 2nd Asia International Conference on Modelling and Simulation, AMS 2008, 2008, pp. 570–573, doi: 10.1109/AMS.2008.77.
[8] W. Dargie and C. Poellabauer, Fundamentals of Wireless Sensor Networks. 2010.
[9] P. Huang, L. Xiao, S. Soltani, M. W. Mutka, and N. Xi, “The evolution of MAC protocols in wireless sensor networks: A survey,” IEEE Commun. Surv. Tutorials, vol. 15, no. 1, pp. 101–120, 2013, doi: 10.1109/SURV.2012.040412.00105.
[10] K. Langendoen and N. Reijers, “Distributed localization in wireless sensor networks: a quantitative comparison,” Comput. Networks, vol. 43, no. 4, pp. 499–518, Nov. 2003, doi: 10.1016/S1389-1286(03)00356-6.
[11] F. Gherardi and L. Aquiloni, “Sexual selection in crayfish: A review,” Crustac. Monogr., vol. 15, no. August, pp. 213–223, 2011, doi: 10.1163/ej.9789004174252.i-354.145.
[12] A. Wyner and J. Ziv, “The rate-distortion function for source coding with side information at the decoder,” IEEE Trans. Inf. Theory, vol. 22, no. 1, pp. 1–10, Jan. 1976, doi: 10.1109/TIT.1976.1055508.
[13] S. Soro and W. Heinzelman, “A Survey of Visual Sensor Networks,” Adv. Multimed., vol. 2009, pp. 1–21, 2009, doi: 10.1155/2009/640386.
[14] M. Malathi, “Cloud computing concepts,” in 2011 3rd International Conference on Electronics Computer Technology, 2011, vol. 6, pp. 236–239, doi: 10.1109/ICECTECH.2011.5942089.
[15] I. F. Akyildiz, T. Melodia, and K. R. Chowdhury, “Wireless multimedia sensor networks: applications and testbeds,” in Proceedings of the IEEE, 2008, vol. 96, no. 10, pp. 1588–1605, doi: 10.1109/JPROC.2008.928756.
[16] M. Ahmadi and S. M. Jameii, “A Secure Routing Algorithm for Underwater Wireless Sensor Networks,” Int. J. Eng., vol. 31, no. 10, pp. 1659–1665, Oct. 2018, doi: 10.5829/ije.2018.31.10a.07.
[17] M. Quaritsch, B. Rinner, and B. Strobl, “Improved agent-oriented middleware for distributed smart cameras,” 2007 1st ACM/IEEE Int. Conf. Distrib. Smart Cameras, ICDSC, no. May 2014, pp. 297–304, 2007, doi: 10.1109/ICDSC.2007.4357537.
[18] A. Doblander, A. Zoufal, and B. Rinner, “A novel software framework for embedded multiprocessor smart cameras,” ACM Trans. Embed. Comput. Syst., vol. 8, no. 3, pp. 1–30, Apr. 2009, doi: 10.1145/1509288.1509296.
[19] S. R. Taghizadeh and S. Mohammadi, “LEBRP - A lightweight and energy balancing routing protocol for energy-constrained wireless ad hoc networks,” Int. J. Eng. Trans. A Basics, vol. 27, no. 1, pp. 33–38, 2014, doi: 10.5829/idosi.ije.2014.27.01a.05.
[20] C.-F. Lin, S.-M. Yuan, M.-C. Leu, and C.-T. Tsai, “A Framework for Scalable Cloud Video Recorder System in Surveillance Environment,” in 2012 9th International Conference on Ubiquitous Intelligence and Computing and 9th International Conference on Autonomic and Trusted Computing, 2012, pp. 655–660, doi: 10.1109/UIC-ATC.2012.72.
[21] J. Kim, N. Park, G. Kim, and S. Jin, “CCTV Video Processing Metadata Security Scheme Using Character Order Preserving-Transformation in the Emerging Multimedia,” Electronics, vol. 8, no. 4, p. 412, Apr. 2019, doi: 10.3390/electronics8040412.
[22] A. Tekeoglu and A. S. Tosun, “Investigating Security and Privacy of a Cloud-Based Wireless IP Camera: NetCam,” in 2015 24th International Conference on Computer Communication and Networks (ICCCN), 2015, vol. 2015-Octob, pp. 1–6, doi: 10.1109/ICCCN.2015.7288421.
[23] L. Valentín, S. A. Serrano, R. Oves García, A. Andrade, M. A. Palacios-Alonso, and L. Enrique Sucar, “A CLOUD-BASED ARCHITECTURE FOR SMART VIDEO SURVEILLANCE,” ISPRS - Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLII-4/W3, no. 4W3, pp. 99–104, Sep. 2017, doi: 10.5194/isprs-archives-XLII-4-W3-99-2017.
[24] K. K. Basu, “Organisational culture and leadership in ERP implementation,” Int. J. Strateg. Chang. Manag., vol. 6, no. 3/4, p. 292, 2015, doi: 10.1504/IJSCM.2015.075919.
[25] D. Bijwe, “International Journal of Computer Science and Mobile Computing Database in Cloud Computing-Database-as-a Service (DBaas) with its Challenges,” Int. J. Comput. Sci. Mob. Comput., vol. 4, no. 2, pp. 73–79, 2015.
[26] García S, Grill M, Stiborek J, Zunino A, An Empirical Comparison of Botnet Detection Methods, Computers & Security (2014), doi: 10.1016/j.cose.2014.05.011.