Machine Learning-Based Insider Threat Detection For Enhanced Organizational Security

Authors: Tuniki Pravalika, Panuganti Lavanya

Abstract: The potential compromise of sensitive information and company assets by employees is a major concern for any firm. Robust ML algorithms that can handle complicated and biassed data are necessary for the threat detection process. Some of the ML models that are tested in this study using the well-known CERT dataset include Logistic Regression, Decision Trees, Random Forest, SVM, KNN, Naïve Bayes, Adaboost, and XGBoost. Approaches like SMOTE, which deal with problems brought on by data imbalance, emphasise the need of a balanced dataset. A 97.5% success rate in detecting insider threats was achieved using Random Forest and Adaboost, according to the data. This study lays the groundwork for more trustworthy organisational security measures by improving approaches for identifying insider threats and offering a systematic evaluation of model performance. A few of the terms that come up include SMOTE, CERT, insider threat detection, and machine learning.

DOI: https://doi.org/10.5281/zenodo.20679357