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Jeyalakshmi, K.
- Security Enhancement for Data Transmission in Mobile AD-HOC Networks
Authors
Source
International Journal of Innovative Research and Development, Vol 2, No 13 (2013), Pagination:Abstract
Mobile Ad-hoc Networks is one of the most popular and widely usable wireless technologies. Among so many wireless standards, Ad-hoc technology won the favor of people because of its lower construction and operating costs, higher data rate, farther transmission distance and better extensibility, etc. But it can be less secure than wired connections because an intruder does not need a physical connection. So security is a key issue in data transmission through this network. In this research work an integrated cryptographic scheme has been proposed to overcome the security problems and to enhance the security of Mobile Ad-hoc Network. This scheme is based on DES, RSA and Rijndael algorithm. These three algorithms are used to generate the key to encrypt the data. Among the three algorithms, Rijndael algorithm has variable block and key length. Since the extension of blocks is possible in Rijndael algorithm, we can extend the block length and key length by multiples of 32bits in order to avoid the loss of data while transferring the biggest files.
Keywords
Ad-hoc, DES, RSA, Rijndael algorithm- Ontology Based Data Unit Similarity With Combining Tag And Value For Data Extraction And Alignment
Authors
Source
International Journal of Innovative Research and Development, Vol 2, No 10 (2013), Pagination:Abstract
Web database extraction is used to retrieve relevant information from the query result page. By combining tag and value one can extracts data from query result pages by first identifying and segmenting the query result records (QRRs) in the query result pages and then aligning the segmented QRRs into a table. But combining tag and value similarity measure doesn’t handle non-contiguous QRR. To overcome this problem a novel method is proposed to display the most distinct query records from user’s query result pages. In this method, First distinct tags are extracted from the result records to build the tag vector table, and then the similarity between each record is found using several similarity methods. Finally the values of similar records are combined and aligned using ontology based alignment.