Description

To detect malicious insiders is very challenging as the methods of deception become more and more sophisticated. These insiders were the most damaging with 43 percent of the respondents reporting that their organization suffered data loss. Of the attacks, 16 percent were caused by theft of sensitive data and 15 percent by exposure of confidential data. New concept of M-Score has proposed to measure misuseability weight, to reduce misuse of data in databases by insiders. We describe four dimensions of misuseability. The first two dimensions are related to entities that appear in the data, the last two dimensions are related to the information that is exposed about these entities. M-Score value stand for sensitive level of data exposed by insiders, several of properties and dimensions too analyzed. Measuring M-Score value, we proposed algorithm M-Score. This algorithm considers and measures different aspects related to the misuseability of the data in order to indicate the true level of damage data exposed by insiders. The M-score measure is tailored for tabular data sets and cannot be applied to non-tabular data. We provide the formal definitions for the M-score. Without loss of generality, we assume that only a single database exists. The measure can be easily extended to cope with multiple databases.