A Probabilistic Approach for Guilty Agent Detection using Bigraph after Distribution of Sample Data

GUIM‐SMD: guilty user identification model using summation matrix‐based distribution

“…To upgrade the performance, it has turned into essentiality for an enterprise to share its critical data among numerous allegedly assured entities interior or exterior to its preface [1, 2 ] However, this shared data can be exposed by the malevolent entity that may induce a severe threat to the enterprise's privacy, confidentiality as well as functionality [3–5 ]. According to risk‐based security's ‘ RBS’ report, almost 7.9 billion data records caused by 5183 data breaches have been compromised surprisingly worldwide within the first nine months of 2019 only, which were 112 and 33.3% up, respectively, as compared to the same period in 2018 [6 ].…”

SELI: statistical evaluation based leaker identification stochastic scheme for secure data sharing

“…Thus, data protection has become a challenging task in the area of information security. The identification of malicious entities can significantly assist to overcome the challenge existing in the area [7, 8]. Therefore, it attracts the researchers to propose models that recognise the guilty entity causing leakage of data for action against this entity.…”

“…The methods based on the probabilistic approach have been proposed in [7, 19, 28–32]. These methods estimated the probability of being malicious for guilty agent identification.…”

“…The method is utilised by many researchers in their models [28–31], however, as per the best of authors' knowledge, no one could outperform the work. An approach for the detection of the guilty agent is presented in [7]. This approach dealt with the sample data requests where the sample data is distributed by utilising the distribution mechanism.…”

A Comparative Study of the Approach Provided for Preventing the Data Leakage

“…The researchers discovered that despite the security policies, procedures, and tools currently in place, employees around the world are engaging in risky behaviours that put corporate and personal data at risk [14], [15]. Organizations provide easy access to databases for information sharing, storage and compression technology has allowed for more powerful (high-risk) endpoints [16], [17]. An 80-MB mobile device now holds 6000 Microsoft Word documents or 7, 20,000 emails, and new 64-GB removable devices allow an entire hard drive to be copied onto a device same as the size of a pack of gum.…”