Srikanth Bethu

2papers

2 Papers

CRNov 23, 2013
Comparison analysis in Multicast Authentication based on Batch Signature (MABS) in Network Security

Srikanth Bethu, K Kanthi Kumar, MD Asrar Ahmed et al.

Conventional block-based multicast authentication schemes overlook the heterogeneity of receivers by letting the sender choose the block size, divide a multicast stream into blocks, associate each block with a signature, and spread the effect of the signature across all the packets in the block through hash graphs or coding algorithms. The correlation among packets makes them vulnerable to packet loss, which is inherent in the Internet and wireless networks. Moreover, the lack of Denial of Service (DoS) resilience renders most of them vulnerable to packet injection in hostile environments. In this paper, we propose a novel multicast authentication protocol, namely MABS, including two schemes. The basic scheme (MABS-B) eliminates the correlation among packets and thus provides the perfect resilience to packet loss, and it is also efficient in terms of latency, computation, and communication overhead due to an efficient cryptographic primitive called batch signature, which supports the authentication of any number of packets simultaneously.so we discuss their comparisons and performance evaluation of Packet Loss, Comparisons over Lossy Channels, Comparisons of Signature Schemes, computationational overheads etc.

IRNov 22, 2013
Text Classification and Distributional features techniques in Datamining and Warehousing

Srikanth Bethu, G Charless Babu, J Vinoda et al.

Text Categorization is traditionally done by using the term frequency and inverse document frequency.This type of method is not very good because, some words which are not so important may appear in the document .The term frequency of unimportant words may increase and document may be classified in the wrong category.For reducing the error of classifying of documents in wrong category. The Distributional features are introduced. In the Distribuional Features, the Distribution of the words in the whole document is analyzed. Whole Document is very closely analyzed for different measures like FirstAppearence, Last Appearance, Centriod, Count, etc.The measures are calculated and they are used in tf*idf equation and result is used in k- nearest neighbor and K-means algorithm for classifying the documents.