Open AccessInsider Threat Detection using Binary Classification Algorithms
Author(s) -
Tolulope O. Oladimeji,
C. K. Ayo,
Sunday Eric Adewumi
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1107/1/012031
Subject(s) - insider threat , insider , scope (computer science) , computer science , binary classification , binary number , strengths and weaknesses , statistical classification , artificial intelligence , machine learning , algorithm , computer security , political science , psychology , mathematics , support vector machine , social psychology , law , arithmetic , programming language
The Insider Threat Detection(ISTD), is commonly referred to as the silent killer of organizations. The impact is greatly felt because it is usually perpetrated by existing staff of the organization. This makes it very difficult to detect or can even go undetected. Several authors have researched into this problem but no best solution has been discovered. This study therefore considers the insider problem as a classification problem. It provides a lay man’s understanding of a typical classification problem as faced in the insider threat detection research scope. It then highlights five (5) commonly used binary classification algorithms, stating their strengths and weaknesses. This work will help researchers determine the appropriate algorithm to consider for the employee dataset available for classification.