A Methodology for Identification of the Guilt Agent based on IP Binding with MAC using Bivariate Gaussian Model

Abstract

Enormous increase in data in the current world presents a major threat to the organization. Most of the organization maintains some sort of data that is sensitive and must be protected against the loss and leakage. In the IT field, the large amount of data will be exchanged between the multiple points at every moment. During this allocation of the data from the organization to the third party, there are enormous probabilities of data loss, leakages or alteration. Mostly an email is being utilized for correspondence in the working environment and from web-based like logins to ledgers; thereby an email is turning into a standard business application. An email can be abused to leave organization's elusive information open to trade off. Along these lines, it might be of little surprise that muggings on messages are normal and these issues need to be addressed. This paper completely focuses on the concept of data leakage, the technique to detect the data leakage and prevention.

Identification of Information Leakage and Guilt Agent by using MAC-IP Binding and Recursive Partitioning Algorithm to Modulate the Uncertainty in the Organization’s Network

“…Data Set that was taken for the exploratory is NUIX which utilized the EDRM ENRON PST informational collection that contains: 1.3 million email, messages and connections from previous ENRON staff having 168 Microsoft standpoint .PST documents that was right around 40 GB of information [10]. This contained numerous examples of private, wellbeing and monetary information and individual data each one of those sends was sent and got by staff of Enron in the advancement of everyday business.…”