Indian Joint Cipher Bureau

About: Indian Joint Cipher Bureau is a based out in . It is known for research contribution in the topics: CBC-MAC & Block cipher. The organization has 2 authors who have published 6 publications receiving 53 citations.

TWIS --- A Lightweight Block Cipher

Abstract: A new 128-bit block cipher, TWIS is proposed It uses key size of 128-bits The design targets to software environment for resource constrained applications It is inspired from existing block cipher, CLEFIA Although the proposed design uses less resources as compared to CLEFIA, it compares favorably with CLEFIA in terms of security provided

Block cipher identification using support vector classification and regression

Abstract: In this paper, we propose two approaches for identification of block ciphers using support vector machines. Identification of the encryption method for block ciphers is considered as a pattern classification task. In the first approach, the cipher text is given as input to the classifier. In the second approach, the partially decrypted text derived from a cipher text is given as input to the classifier. Support vector regression based hetero-association model is used to derive the partially decrypted text. The cipher text and partially decrypted text are considered as documents and the task of identification of encryption method is considered as a document categorization task. We address the issues in representing a document by a feature vector. Three methods are considered for representation of a document by a feature vector. In the first method, a document is represented as a vector of integers. In the second method, a document is represented by a block level similarity based feature vector. Su...

Identification scheme for romanized Indian languages from their plain and ciphered bit stream

Abstract: Identification of the Indian languages, when they are communicated in their plain bit stream after Romanizing their script has been dealt. An Attempt has also been made to identify them from their enciphered bit stream obtained through standard encryption schemes. In this context plain and cipher bit stream of four Indian languages viz. Hindi, Punjabi, Oriya and Bengali have been studied. A novice method proposed earlier [6] has been extended for extraction of statistical features. Several other feature extraction and features selection technique have been used for experimenting with four classifiers and finally the results are summarized. Maximum Likelihood Classifier (MLC) has performed better than Minimum Distance Classifier (MDC), Linear Statistical Classifier (LSC) and Piecewise Linear Classifier (PLC) in terms of performance accuracy and consistency.

Classification Models for Symmetric Key Cryptosystem Identification

Abstract: The present paper deals with the basic principle and theory behind prevalent classification models and their judicious application for symmetric key cryptosystem identification These techniques have been implemented and verified on varieties of known and simulated data sets After establishing the techniques the problems of cryptosystem identification have been addressed Defence Science Journal, 2012, 62(1), pp38 -45 , DOI:http://dxdoiorg/1014429/dsj 621440

Comparative performance of the classifiers for cryptosystem identification

Abstract: In the present work the problem of cryptosystem identification from their cipher texts have been addressed. The supervised classification models from statistical decision theory and Artificial Neural Network have been employed for the purpose. These classification models have been validated on known data sets from UCI repository. After validation the models have been used for crypto system identification. Several feature extraction and selection techniques have been made use of for carrying out the comparative performance of the classifiers.