Abstract
Error-correcting codes are used to encode data prior to transmission through noisy channel for their error-free data communication. In cryptographic secure communication, error-free key transmission is very essential for getting same key as used at transmitting end for message decryption at receiving end. As BCH codes, Golay codes, and Hamming codes are normally used in modern communication to encode data for error-free transmission over noisy channel, classification of distorted encoded data is an important activity and is very much required to decode intercepted data. We consider a statistical pattern recognition approach for classification of coded messages by applying multidimensional feature vectors and minimum distance criteria. The classification results obtained as shown in simulation results are quite encouraging, and we could classify such coded data with minimum 85 % and maximum up to 100 % success rate.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
MacWilliams, F.J., Sloane, N.J.: The Theory of Error-Correcting Codes. Elsevier Science, North-Holland (1992)
Trappe, W., Washington, L.C.: Introduction to Cryptography with Coding Theory. Pearson Prentice Hall, Upper Saddle River (2006)
Bose, R.: Information Theory, Coding and Cryptography. McGraw-Hill, New York (2008)
Beker, P.W.: Recognition of Patterns: Using Frequency of Binary Words. Springer, New York (1978)
Kant, S., Verma, N.: An effective source recognition algorithm: Extraction of significant binary words. Pattern Recogn. Lett. 21, 981–988 (2001)
Kant, S., Verma, V., Verma, N., Das, B.K.: Identification scheme for romanized Indian languages from their plain and ciphered bit stream. J. Disc. Math. Sci. Crypt. 13(4), 329–345 (2010)
Maiya, D., Ratan, R., Bhateja, A. K., Bhateja, A.: Multimedia classification using ANN approach. In: Proceedings of SocProS 2012, Springer’s AISC 236, pp. 905–910 (2014)
Valembois, A.: Detection and recognition of a binary linear code. Disc. Appl. Math 111(1–2), 199–218 (2001)
Zhou, J., Huang, Z., Liu, C., Su, S., Zhang, Y.: Information dispersion entropy based blind recognition of binary BCH codes in soft decision situations. J. Entropy 15, 1705–1725 (2013)
Schalkoff, R.: Pattern Recognition: Statistical, Structural and Neural Approaches. Wiley, Hoboken (1992)
Tou, J.T., Gonzalez, R.C.: Pattern Recognition Principles. Addison-Wesley, Reading (1974)
Sing-Tze, B.: Pattern Recognition. Marcel Dekker Inc., New York (1984)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley Interscience, Hoboken (2001)
Theodoridis, S., Kourtroumbas, K.: Pattern Recognition. Academic Press, Waltham (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer India
About this paper
Cite this paper
Asthana, R., Sharma, A., Ratan, R., Verma, N. (2015). Classification of Error-Correcting Coded Data Using Multidimensional Feature Vectors. In: Das, K., Deep, K., Pant, M., Bansal, J., Nagar, A. (eds) Proceedings of Fourth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 336. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2220-0_24
Download citation
DOI: https://doi.org/10.1007/978-81-322-2220-0_24
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-2219-4
Online ISBN: 978-81-322-2220-0
eBook Packages: EngineeringEngineering (R0)