Behavioral Analytics for Continuous Insider Threat Detection in Zero-Trust Architectures

Insider threats are a particularly tricky cybersecurity issue, especially in zero-trust architectures (ZTA) where implicit trust is removed. Although the rule of thumb is never trust, always verify, attackers can still use legitimate credentials and impersonate the standard user activity. In response, behavioral analytics with machine learning (ML) can help monitor the user activity continuously and identify the presence of anomalies. This introductory framework makes use of the CERT Insider Threat Dataset for data cleaning, normalization, and class balance using the Synthetic Minority Oversampling Technique (SMOTE). It also employs Principal Component Analysis (PCA) for dimensionality reduction. Several benchmark models, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Bayesian Network (Bayes Net), were used to develop and evaluate the AdaBoost classifier. Compared to SVM (90.1%), ANN (94.7%), and Bayes Net (94.9), AdaBoost achieved higher performance with a 98.0% ACC, 98.3% PRE, 98.0% REC, and F1-score (F1). The Receiver Operating Characteristic (ROC) study, which provided further confirmation of its strength, yielded an Area Under the Curve (AUC) of 0.98. These results prove the effectiveness and dependability of AdaBoost-based behavioral analytics as a solution to reinforcing continuous insider threat detection in zero-trust settings.