An Approach for Risk Estimation in Information Security Using Text Mining and Jaccard Method

Abstract

Involvement of digital information in almost of enterprise sectors makes information having value that must be protected from information leakage. In order to obtain proper method for protecting sensitive information, enterprise must perform risk analysis of threat. However, enterprises often get limitation in measuring risk related information security threat. Therefore, this paper has goal to give approach for estimating risk by using information value. Techniques for measuring information value in this paper are text mining and Jaccard method. Text mining is used to recognize information pattern based on three classes namely high business impact, medium business impact and low business impact. Furthermore, information is given weight by Jaccard method. The weight represents risk levelof information leakage in enterprise quantitatively. Result of comparative analysis with existing method show that proposed method results more detailed output in estimating risk of information security threat.

A semantic web services discovery approach integrating multiple similarity measures and k-means clustering

“…The Jaccard Index (also named the Jaccard Similarity Coefficient) is a statistic utilized to compare the diversity and similarity of the sample set [37]. This Index is defined as present in (6), as the number of shared objects divided by the total number of objects, minus the number of common objects.…”