Abstract: This paper, proposes an efficient way to secure the data and file in cloud server. Basically in cloud server when data is stored it is not sure whether the data is having security or not. This concept drastically reduces the communication and storage overhead as compared to the traditional replication based file distribution techniques. By utilizing the IP address restriction with data and providing key to distributed data which is stored on cloud server, whenever data corruption has been detected during the storage correctness verification, our scheme can almost guarantee the simultaneous localization of data errors, i.e., the identification of the misbehaving server(s). These scheme further supports secure and efficient dynamic operations on data blocks, including: data update, delete and append. This methodology ensure the data security on cloud computing so that any hacker cannot use the others data or download. On this paper the restriction is been done by IP address and key to ev

• Publication Date: 13-Jun-2013
• DOI: 10.15224/978-981-07-6935-2-51
• Views: 0
• Downloads: 0

Abstract: Today the web contains a huge amount of information provided as html and xml pages and their number is growing rapidly with expansion of the web. In Web text mining, the text extraction and filtering of extracted content is the foundation of text mining. Automatic Classification of text is a semi-supervised machine learning task that automatically classify a given document to a set of pre-defined categories based on its features and text content. This paper explains a generic strategy for automatic classification of web pages that deals with unstructured and semi-structured text. This work classified the datasets into different labeled classes using kNN and Naïve Bayesian classification techniques. The experimental evaluation concluded that kNN has better accuracy, precision and recall value as compared to Naïve Bayesian classification. This paper presents a unified approach that is able to provide robust classification and validation of web pages to different categories

• Publication Date: 13-Jun-2013
• DOI: 10.15224/978-981-07-6935-2-52
• Views: 0
• Downloads: 0