Insider Threat Detection Based on Anomalous Behavior of User for Cybersecurity
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In today’s competitive world, business security is essential. To secure the business processes and confidential data, organizations have to protect the system by implementing new policies and techniques to detect the threats and control it. Threats for cybersecurity are classified into two types, outsider and insider threats. Both threats are very harmful to the organization. These may convert into a severe attack on the systems upon future. Outsider threats have to take more effort to break the security system. But inside users are those who are privileged to access the system within the organization. As data form is digital, it is straightforward to transfer from one to another. Data leakage, theft, and sharing are easy for insiders. Therefore, there is a need to research in this domain. In this proposed paper, a study of insider threat detection based on the anomalous behavior of the user for cybersecurity is presented. The data processing and anomaly detection algorithms are performed for insider threat detection by researchers. This research paper presented a study on insider threat detection based on the anomalous behavior of the user for cybersecurity.
KeywordsCybesecurity Insider threats Anomalous behavior Machine learning Data leakage Bipartite graph
I wish to acknowledge the Software Engineering Institute of Carnegie Mellon University and Exact Data LLC for making available data for the research study.
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