IMS Manthan (The Journal of Mgt., Comp. Science & Journalism)

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Target Classification Architectural Scheme for Secure Decision Making in Network Centric Environment with MPLS-VPN Architecture


Affiliations
1 Research Centre Imarat, Defence Research & Development Organization, Hyderabad, India
2 Institute of Radiophysics and Electronics, Calcutta University, Kolkata, India
3 ECSU, Indian Statistical Institute, Kolkata, India
4 Institute of Cybernetic Systems and Information Technology, Kolkata, India
5 Defence Research & Development Organization, New Delhi, India
     

   Subscribe/Renew Journal


We present a new approach for military decision-making in a network centric environment in perspective of warfare information received from sensors of several networks geographically dispersed. Sensors used across various networks are different types, which generate data that need to be classified for target identification in real time. We also describe the network architecture and secure data communication using Multi Protocol Level Switching (MPLS)-Virtual Private Network (VPN) techniques that enables interoperability, convergence in diverse communication structures across land, air and sea to protect war assets and go offensive when required, which depends on effective communication, information assurance and information security across a challenging terrain and various theatres of battlefield arena. Our proposed network architecture and classification framework ensures packet level authentication that enables the network to restructure itself after a large scale or dedicated attack. It ensures scalability of the system as a whole. It is resilient to data corrupted by enemy's countermeasures and can perform even if a sensor is jammed. Also, the diversity in the classifiers of our ensemble system allows different decision boundaries to be generated by using slightly different training parameters, such as different training datasets. The classification approach primarily applies to a single target or multiple targets that are separated sufficiently in space and/or time.

Keywords

Network Centric Warfare, MPLS-VPN, Belief Functions, Decision Support System, Datafusion, Bayesian Theory, Dempster-Shafer Theory
Subscription Login to verify subscription
User
Notifications
Font Size


  • Shi Fu-li; Lei Yong-lin; Zhu Yi-fan, “A Military Communication Supernetwork Structure Model for Netcentric Environment”, IEE Conf. Computational and Information Sciences (ICCIS), 2010 International Conference on, PP. 33 – 36, 2010.

  • Sadjadi, F., “ Improved target classification using optimum polarimetric SAR signatures”, IEEE Transactions on Aerospace and Electronic Systems, I. VOL. 38(1) ,PP. 38 – 49, 2002.

  • Challa, S.; Pulford, G.W, “ Joint target tracking and classification using radar and ESM sensors “,IEEE Transactions on Aerospace and Electronic Systems, Vol. 37(3), PP. 1039 – 1055 , 2001.

  • Efraim Turban, Jay E. Aronson, Ting-Peng Liang, "Decision Support Systems and Intelligent Systems", Prentice Hall, 2005.

  • Arundhati Bhattacharyya, Chandan Mazumdar and V.K. Saraswat. “A Real-Time Robust Decision Support System for Network Centric Warfare,” Proc. 8th European Conference on Information Warfare and Security, Lisbon, Portugal, Academic Publishing Limited, pp 9-19, 2009.

  • Pal S.K., Dutta Majumder D.” Fuzzy mathematical approach to pattern recognition”, Wiley, New York , 1986.

  • V V S Sarma, and Savitri Raju, “Multisensor Data Fusion and Decision Support for Airborne Target Identification” IEEE Transaction on Systems, Man, And Cybernetics, vol. 21, no.5, September/October 1991, pp. 1224-1230.

  • Min Su Lee and Seung Soo Park, “Comparative Analysis of Classification Methods for Protein Interaction Verification System”, ADVIS, LNCS4243, Springer-Verlag, , pp. 227-236, 2006.

  • Richard R. Brooks, Parameswaran Ramanathan and Akbar M. Sayeed, “Distributed Target Classification and Tracking in Sensor Networks”, Proc. IEEE, vol. 91, no. 8, pp 1163-1171, 2003.

  • P.L. Bogler, “Shafer-Dempster Reasoning with Applications to Multisensor Target Identification Systems”, IEEE Trans. On Systems, Man and Cybernetics, vol. SMC-17, no. 6, pp. 968-977, 1987.

  • Barry R. Cobb, and Prakash P. Shenoy, “A Comparison of Bayesian and Belief Function Reasoning”, Information Systems Frontiers, vol. 5, no. 4, Dec. 2003, pp. 345–358.

  • Dennis M. Buede and Paul. Girardi, “A Target Identification Comparison Of Bayesian and Dempster-Shafer Multisensor Fusion”, IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans, vol. 27, no.5, PP. 569 - 577 , 1997.

  • Dongli Zhang; Ionescu, D,” Measurement and Control of Packet Loss Probability for MPLS VPN Services”, Instrumentation and Measurement, IEEE Transactions on, Volume: 55 , Issue: 5 , Page(s): 1587 – 1598, 2006.

  • Daugherty, B.; Metz, C,” Multiprotocol label switching and IP. Part I. MPLS VPNs over IP tunnels” Internet Computing, IEEE, Vol. 9(3) PP. 68 – 72, 2005.

  • Jiang Dong, Dafang Zhuang, Yaohuan. Huang and Jingying Fu, “Advances in Multi-sensor Data Fusion: Algorithms and Applications”, Sensors 2009, pp. 7771-7784, 2009.

  • Kuusniemi, H.; Liang Chen; Ruotsalainen, L.; Ling Pei; Yuwei Chen; Ruizhi Chen, ”Multi-sensor multi-network seamless positioning with visual aiding”, Int. Conf. on Localization and GNSS (ICL-GNSS), PP. 146 – 151, 2011.


Abstract Views: 362

PDF Views: 0




  • A Target Classification Architectural Scheme for Secure Decision Making in Network Centric Environment with MPLS-VPN Architecture

Abstract Views: 362  |  PDF Views: 0

Authors

A. Bhattacharyya
Research Centre Imarat, Defence Research & Development Organization, Hyderabad, India
S. Karan
Institute of Radiophysics and Electronics, Calcutta University, Kolkata, India
D. Dutta Majumder
ECSU, Indian Statistical Institute, Kolkata, India
V. K. Saraswat
Institute of Cybernetic Systems and Information Technology, Kolkata, India
C. Mazumdar
Defence Research & Development Organization, New Delhi, India

Abstract


We present a new approach for military decision-making in a network centric environment in perspective of warfare information received from sensors of several networks geographically dispersed. Sensors used across various networks are different types, which generate data that need to be classified for target identification in real time. We also describe the network architecture and secure data communication using Multi Protocol Level Switching (MPLS)-Virtual Private Network (VPN) techniques that enables interoperability, convergence in diverse communication structures across land, air and sea to protect war assets and go offensive when required, which depends on effective communication, information assurance and information security across a challenging terrain and various theatres of battlefield arena. Our proposed network architecture and classification framework ensures packet level authentication that enables the network to restructure itself after a large scale or dedicated attack. It ensures scalability of the system as a whole. It is resilient to data corrupted by enemy's countermeasures and can perform even if a sensor is jammed. Also, the diversity in the classifiers of our ensemble system allows different decision boundaries to be generated by using slightly different training parameters, such as different training datasets. The classification approach primarily applies to a single target or multiple targets that are separated sufficiently in space and/or time.

Keywords


Network Centric Warfare, MPLS-VPN, Belief Functions, Decision Support System, Datafusion, Bayesian Theory, Dempster-Shafer Theory

References