Rough set analysis and cloud model algorithm to automated knowledge acquisition for classification Iris to chieve high security

Most of Intrusion Detection Systems uses all data features to detect an intrusion. Very little work addresses the importance of having a small feature subset in designing an efficient intrusion detection system. Some features are redundant and some contribute little to the intrusion detection process. Purpose of this study is to investigate the effectiveness of Rough Set Theory in identifying the important features in building an Intrusion detection system. Rough Set is also used to classify Iris data. Here, we used CASIA V1.0 (CASIA-IrisV1) data, presents In this paper, a new algorithm, Decision Tree Construction based on Rough Set Theory under Characteristic Relation (DTCRSCR), is proposed for mining classification knowledge from incomplete information systems. The algorithm is then used in iris classification. Its idea is to select the attribute whose weighted mean roughness under the characteristic relation as current splitting node. Our framework RST-DTCRSCR method result has a higher accuracy as compared to either full feature or entropy.