Abstract
Current communication systems are very efficient and being used conveniently for secure exchange of vital information. These communication systems may be misused by adversaries and antisocial elements by capturing our vital information. Mostly, the information is being transmitted in the form of plain English text apart from securing it by encryption. To avoid losses due to leakage of vital information, one should not transmit his vital information in plain form. For monitoring of huge traffic, we require an efficient plain English text identifier. The identification of short messages in which words are written in short by ignoring some letters as in mobile messages is also required to monitor. We propose an efficient plain English text identifier based on Fuzzy measures utilizing percentage frequencies of most frequent letters and least frequent letters as features and triangular Fuzzy membership function. Presented method identifies plain English text correctly even, the given text is decimated/discontinuous and its length is very short, and seems very useful.
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References
Stephen, G.W.: Digital Modulation and Coding. Prentice Hall, New Jersey (1996)
Katzenbeisser, S., Petitcolas, F.A.P.: Information Hiding Techniques for Steganography and Digital Watermarking. Artech House, London (2000)
Ratan, R., Madhavan, V.C.E.: Steganography Based Information Security. IETE Tech. Rev. 19(4), 213–219 (2002)
Menezes, A., Van Oorschot, P., Vanstone, S.: Handbook of Applied Cryptography. CRC Press, USA (1996)
Stallings, W.: Cryptography and Network Security. Prentice Hall, Englewood Cliffs (2011)
Han, J., Kamber, M.L.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2006)
Sebastiani, F.: Machine learning in automated text categorization. ACM Comput. Surv. 34(1), 1–47 (2002)
Wong, A.K.S., Lee, J.W.T., Yeung, D.S.: Using complex linguistic features in context - sensitive text classification techniques. Proceedings of the 4th IEEE International Conference on Machine Learning and Cybernetics 5, 3183–3188 (2005)
Simanjuntak, D.A., Ipung, H.P., Lim, C., Nugroho, A.S.: Text classification techniques used to facilitate cyber terrorism investigation. Proceedings of the 2nd IEEE International Conference on Advances in Computing, Control and Telecommunication Technologies (ACT), 198–200 (2010).
Upasana, Chakrabarty, S.: A survey of text classification techniques for e-mail filtering. Proceedings of the 2nd IEEE International Conference on Machine Learning and Computing (ICMLC), 32–36 (2010).
Cavnar, W.B., Trenkle, J.M.: N-Gram based text categorization, pp. 161–175. Proceedings of the Symposium on Document Analysis and Information Retrieval. Las Vegas, NV pp (1994)
Grefenstette, G.: Comparing two language identification schemes. Proceedings of the International Conference on Statistical Analysis of Textual Data JADT-95 (1995).
Kullback, S.: Statistical Methods in Cryptanalysis. Aegean Park, CA (1976)
Friedman, W.F.: Elements of Cryptanalysis. Aegean Park, CA (1976)
Anderson, A., Rosenfield, E.: Neuro Computing. MIT Press, MA (1988)
Dubois, D., Prade, H.: Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York (1980)
Bezdek, J.C., Pal, S.K.: Fuzzy Models for Pattern Recognition. IEEE Press, New York (1992)
Puri, S., Kaushik, S.: A technical study and analysis on fuzzy similarity based models for text classification. Int. J. Data Min. Knowl. Manag. Process 2(2), 1–15 (2012).
Ratan, R., Saxena, P.K.: Algorithm for the restoration of distorted text documents. Proceedings of the International Conference on Computational Linguistics, Speech and Document Processing (ICCLSDP) (1998)
Yang, H.H., Moody, J.: Feature selection based on joint mutual information. J. Comput. Intell. Met. Appl. Int. Comput. Sci. Conv. 13, 1–8 (1999)
Saxena, P.K., Yadav, P., Mishra, G.: Index of garbledness for automatic recognition of plain english texts. Defence Sci. J 60(4), 415–419 (2010)
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Renu, Ravi, Ratan, R. (2014). Live Traffic English Text Monitoring Using Fuzzy Approach. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_97
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DOI: https://doi.org/10.1007/978-81-322-1602-5_97
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