Volume-3 , Issue-1 , Feb 2015, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Face Recognition Using Principal Component Analysis in MATLAB

  Prabhjot Singh and Anjana Sharma

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.3 , Issue.1 , pp.1-5, Jan-2015

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  Abstract
  The paper present an semi-automated program for human face recognition. A self prepared database of different faces is used. Task of removing background from the image is a challenge but on the other hand by implementing Viola-Jones face detection algorithm and by Principal Component analysis it is possible. An application of system can be real time implementation of face recognition system. A robust and reliable form of recognition can be done by using Principal Component analysis. In the process Eigen faces or Eigen values are selected by PCA calculating the nearest face or value and then displaying result. This biometric system has real time application as used in attendance systems.

  Key-Words / Index Term
  Eigenface, Eigenvalues, Detection, PCA, Recognition

  References
  [1] W. Zhao, R. chellappa, P. J. Phillips, Face recognition: A literature survey, “ACM Computing Surveys (CSUR)”, December 2003.
  [2] B.Rashida, Dr. M Munir Ahamed Rabbani, 3d Image Based Face Recognition System.
  [3] Paul Viola, Michael J.Jones, Robust Real-Time Face Detection.
  [4] C. Nagaraju, B srinu, E. Srinivasa Rao, An efficient Facial Features extraction Technique for Face Recognition system Using Local Binary Patterns.
  [5] Bhabatosh Chanda and Dwijest Dutta Majumder, 2002, Digital Image Processing and Analysis.
  [6] Hussein Rady, “Face Recognition using Principle Component Analysis with Different Distance Classification” (El-Shorouk Academy, Higher Institute for computer & Information Technology, Egypt), IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.10, October 2011.
  [7] Ayushi gupta, Ekta Sharma, Neha sachan and neha Tiwari, “Door Lock System through Face Recognition Using MATLAB”, IJSRCSE ( International Journal of Scientific Research in Computer Science and Engineering), 2013.
  [8] C. Han, “Modular PCA Face Recognition Based on Weighted Average”. Modern Applied Science, Vol. 3, No. 11, November 2009.
  [9] D. Chen, and H. Jiu-qiang, “An FPGA-based face recognition using combined 5/3 DWT with PCA methods”. Journal of Communication and Computer, ISSN 848-7709, USA, Volume 6, No. 10 (Serial No. 59), Oct. 2009.
  [10] H. Moon, and P. Phillips, “Computational and performance aspects of PCA-based face-recognition algorithms”, Perception, 2001, volume 30, pp303-321.
  [11] M. Sharkas, “Applicaion of DCT Blocks with Principal Component Analysis for Face ecognition”, Proceedings of the 5th WSEAS int. Conf. on Signal, Speech and Image Processing, Corfu, Greece, (pp107-111), 2005.
  [12] Sukhvinder Singh, Meenakshi Sharma and Dr. N Suresh Rao, “Accurate Face Recognition Using PCA and LDA”, International Conference on Emerging Trends in Computer and Image Processing (ICETCIP’2011) Bangkok December, 2011.
  

  Citation
  Prabhjot Singh and Anjana Sharma, "Face Recognition Using Principal Component Analysis in MATLAB," International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.1-5, 2015

• Open Access   Article

  Secure Multiparty Protocol for Distributed Mining of Association Rules

  M.Manigandan and K. Aravind Kumar

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.3 , Issue.1 , pp.6-10, Jan-2015

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  Association rule mining is one of the data mining tasks to find the association between the items or among the item sets. The privacy concept arises when the database is in the distributed environment in which each database holder is interested to discover the globally supported association rules by participating themselves in mining process without revealing its individual locally supported association rules. The present leading protocol consumes computational overhead as well as communication overhead in order to preserve the privacy in mining. A new protocol is proposed to reduce the communication cost and computational cost for secure mining in homogeneous databases. The proposed protocol uses the secure sum computation and set union computation for determining global association rules. It is significantly more secure and efficient as compared with the existing protocol.

  Key-Words / Index Term
  Privacy Preserving Data Mining; Frequent Item Sets; Association Rules; Secure Multiparty Protocol; Distributed Mining; Homogeneous Databases; Secure Sum Computation

  References
  [1] R. Agrawal and R. Srikant, “Privacy Preserving Data Mining”, Proc. ACM SIGMOD Conf., pp. 439-450, 2000.
  [2] A. Ben-David, N. Nisan, and B. Pinkas, “Fairplay MP - A System for Secure Multiparty Computation”, Proc. 15th ACM Conf. Computer and Comm. Security (CCS), pp.257-266, 2008.
  [3] J. Brickell and V. Shmatikov, “Privacy Preserving Graph Algorithms in the Semi-Honest Model”, Proc. 11th Int’l Conf. Theory and Application of Cryptology and Information Security (ASIACRYPT), pages 236-252, 2005.
  [4] D.W.L Cheung, V.T.Y. Ng, A.W.C. Fu, and Y. Fu. “Efficient Mining of Association Rules in Distributed Databases”, IEEE Trans. Knowl. and Data Eng., Vol.8, No.6, 911-922, 1996.
  [5] V. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke. “Privacy Preserving Mining of Association Rules”, Proc. Eight ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD), pp.217-228, 2002.
  [6] M. J. Freedman, K. Nissim, and B. Pinkas, “Efficient Private Matching and Set Intersection”, Proc. Int’l Conf. Theory and Application of Cryptographic Techniques (EUROCRYPT), pp.1-19, 2004.
  [7] M. Kantarcioglu and C. Clifton, “Privacy Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data”, IEEE Trans. Knowl. and Data Eng., Vol. 16, No.9, 1026-1037, 2004.
  [8] L. Kissner and D.X. Song, “Privacy Preserving Set Operations”, Proc. 25th Ann. Int’l Cryptology Conf. (CRYPTO), pages 241-257, 2005.
  [9] A. Schuster, R. Wolff, and B. Gilburd, “Privacy Preserving Association Rule Mining in Large Scale Distributed Systems”, Proc. IEEE Int’l Symp. Cluster Computing and the Grid (CCGRID), pp. 411-¬418, 2004.
  [10] Tamir Tassa, “Secure Mining of Association Rules in Horizontally Distributed Database”, IEEE Trans. Knowl. and Data Eng.,Vol.26. No.4, 970-983, 2014.
  [11] J. Vaidya and C. Clifton, “Privacy Preserving Association Rule Mining in Vertically Partitioned Data”, Proc. Eight ACM SIGKDD Int’l Conf. Knowledge Discovery and Data Mining (KDD), pp.639-644, 2002.
  

  Citation
  M.Manigandan and K. Aravind Kumar, "Secure Multiparty Protocol for Distributed Mining of Association Rules," International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.6-10, 2015

• Open Access   Article

  On Mechanism to Prevent Cooperative Black Hole Attack in Mobile Ad Hoc Networks

  Umesh Kumar Singh, Jalaj Patidar and Kailash Chandra Phuleriya

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.3 , Issue.1 , pp.11-15, Jan-2015

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  Abstract
  Wireless or Mobile ad hoc Networks (MANETs) emerged to replace the wired networks. MANETs are extensively used in military and civilian applications. The wireless and dynamic nature of MANETs makes them more vulnerable to security attacks when compared with fixed networks. The existing routing protocols are optimized to perform the routing process without considering the security problems. In this paper, we have examined the effect of black holes attacks on the networks. We have also presented a protocol to identify multiple black holes collaborating with each other and a solution to determine a safe route to protect our network on cooperative black hole attack, while showing the future aspects.

  Key-Words / Index Term
  MANETs, Black Hole, Routing, SAODV, DSR

  References
  [1]. Umesh Kumar Singh, Kailash Phuleriya, Shailja Sharma, D.N. Goswami, A Comparative study of Collaborative Attacks on Mobile Ad-Hoc Networks, International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 8, pp.183-187, August 2014.
  [2]. Umesh Kumar Singh, Lokesh Laddhani and Kailash Chandra Phuleriya, An Analysis of MANET Routing Protocols with Effective Resource Management, International Journal of Emerging Technology and Advanced Engineering, Volume 4, Issue 6, pp. 724-729, June 2014.
  [3]. Umesh Kumar Singh, Kailash Chandra Phuleriya, Shailja Sharma, and D.N. Goswami, On Protocols to Prevent Black Hole Attacks in Mobile Ad Hoc Networks, International Journal of Electronics Communication and Computer Engineering Volume 6, Issue 1, pp. 55-60, year 2015.
  [4]. Pooja Jaiswal and Dr. Rakesh Kumar, Prevention of Black Hole Attack in MANET, IRACST – International Journal of Computer Networks and Wireless Communications (IJCNWC), Vol.2, No5, pp. 599-606, October 2012.
  [5]. Hongmei Deng, Wei Li, and Dharma P. Agarwal, “Routing Security in Wireless Ad Hoc Networks”, University of Cincinnati, IEEE Communications magazine,Vol.40, no.10, October 2002.
  [6]. S. Marti, T. J. Giuli, K. Lai, and M. Baker, Mitigating routing misbehavior in mobile ad hoc networks. In mobile Computing and Networking (MOBICOM), pages 255–265, 2000.
  [7]. P. Michiardi and R. Molva. Preventing denial of service and selfishness in adhoc networks. In Working Session on Security in Ad Hoc Networks, Lausanne, Switzerland, June 2002.
  [8]. Deng H., Li W. and Agrawal, D. P., "Routing security in wireless ad hoc networks," Communications Magazine, IEEE, vol.40, no.10, pp. 70- 75, October 2002.
  [9]. Payal N. Raj1 and Prashant B. Swadas2, “DPRAODV: A Dynamic Learning System against Blackhole Attack in AODV based MANET”, IJCSI International Journal of Computer Science Issues, Vol. 2, 2009.
  [10]. Charles E. Perkins, and Elizabeth M. Royer, “Ad-hoc On-Demand Distance Vector (AODV) routing,” Internet Draft, November 2002.
  [11]. C. E. Perkins and P. Bhagwat, “Highly Dynamic Destination-Sequenced Distance-Vector Routing (DSDV) for Mobile Computers,” Computer Communications Review, pp. 234-244, October 1994.
  [12]. D.Johnson, Y. Hu, and D. Maltz, "The Dynamic Source Routing Protocol (DSR) for Mobile Ad Hoc Networks for IPv4", RFC 4728, 2007.
  [13]. Perkins C.E., Elizabeth M., Royer & Samir R., “Ad hoc On- Demand Distance Vector Routing, IEFT MANET Draft, Charles E. Perkins, Ad Hoc Networking, ISBN 0-201-30976-9, 2003.
  [14]. Raja L., & Santhosh B., “Comparative study of reactive routing protocol (AODV, DSR, ABR and TORA) in MANET”, International Journal of Engineering and Computer Science, Volume 2 Issue 3, PP. 707-718, 2013.
  [15]. Bing Wu, Jianmin Chen, Jie Wu & Mihaela Cardei, A Survey on Attacks and Countermeasures in Mobile Ad Hoc Networks, WIRELESS/MOBILE NETWORK SECURITY Y. Xiao, X. Shen, and D.-Z. Du (Eds.), Springer, pp. 1-38, 2006.
  [16]. Nagpal C., Kumar C., Bhushan B. & Gupta S., A Study of Black Hole Attack on MANET Performance, I. J. Modern Education and Computer Science, pp. 47-53, 2012.
  [17]. Manickam P. ,Baskar T &.Manimegalai D., “Performance Comparisons Of Routing Protocols In Mobile Ad Hoc Networks”, International Journal of Wireless & Mobile Networks (IJWMN) Vol. 3, No. 1, pp. 98-106, 2011.
  [18]. Johnson D. B., Maltz D. & Broch J., “DSR: The Dynamic Source Routing Protocol for Multi-Hop Wireless Ad hoc Network”, C.E. Perkins, Ed., Addison-Wesley, pp.139-172, 2001.
  [19]. Prateek K., Arvind N.& Alaria S., “MANET-Evaluation of DSDV, AODV andDSR Routing Protocol”, International Journal of Innovations in Engineering and Technology (IJIET), Vol. 2 Issue 1, pp.-99-104, 2013.
  [20]. The network simulator. (http://www.isi.edu/nsnam/ns/).
  [21].Umesh Kumar Singh, Kailash Chandra Phuleriya and Rakhi Sunhare, Wireless Sensor Networks: Comparative Study and Analysis of MAC Protocols, International Journal of Computer Networking, Wireless and Mobile Communications (IJCNWMC), Vol. 4, Issue 2, pp. 107-114, Apr 2014.
  

  Citation
  Umesh Kumar Singh, Jalaj Patidar and Kailash Chandra Phuleriya, "On Mechanism to Prevent Cooperative Black Hole Attack in Mobile Ad Hoc Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.11-15, 2015