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

Source: arXiv - 2601.06708v1

Overview

The paper presents a machine‑learning framework that continuously monitors user behavior to spot insider threats inside Zero‑Trust Architectures (ZTA). By combining data‑pre‑processing tricks (SMOTE, PCA) with an AdaBoost ensemble, the authors achieve near‑perfect detection on the widely used CERT Insider Threat Dataset, demonstrating a practical path toward “never trust, always verify” in real‑world networks.

Key Contributions

• End‑to‑end pipeline for insider‑threat detection: data cleaning → class‑balancing (SMOTE) → dimensionality reduction (PCA) → classification.
• AdaBoost‑based ensemble that outperforms classic baselines (SVM, ANN, Bayesian Network) with 98 % accuracy and an AUC of 0.98.
• Comprehensive evaluation using precision, recall, F1‑score, and ROC curves to validate robustness.
• Open‑source reproducibility: the workflow is built on the publicly available CERT Insider Threat Dataset, enabling other teams to replicate or extend the study.

Methodology

1. Dataset preparation – The CERT dataset (synthetic insider‑threat logs) is first cleaned and normalized. Because insider‑threat events are rare, the authors apply SMOTE to synthetically generate minority‑class samples, achieving a balanced training set.
2. Feature reduction – With dozens of raw attributes (file accesses, email counts, login times, etc.), Principal Component Analysis (PCA) compresses the data to the most informative components, cutting noise and speeding up training.
3. Model training – Several baseline classifiers (Support Vector Machine, Artificial Neural Network, Bayesian Network) are trained for comparison. The core model is an AdaBoost ensemble that iteratively combines weak learners (decision stumps) to form a strong predictor.
4. Evaluation – Standard classification metrics (accuracy, precision, recall, F1) and the ROC‑AUC curve are computed on a held‑out test split to assess detection quality and false‑positive rates.

Results & Findings

• AdaBoost consistently beats the baselines across all metrics, indicating superior ability to separate legitimate user activity from malicious insider behavior.
• The high AUC (0.98) shows that the model maintains strong discrimination even when the decision threshold is varied, which is crucial for tuning false‑positive rates in production.

Practical Implications

• Real‑time monitoring: The lightweight nature of decision‑stump learners in AdaBoost makes it feasible to embed the model into security information and event management (SIEM) pipelines for continuous scoring of user actions.
• Zero‑Trust enforcement: Organizations can augment ZTA policies with a behavior‑based “trust score” that automatically revokes or limits access when an anomaly spikes, reducing reliance on static credential checks.
• Scalable to other domains: The same preprocessing (SMOTE + PCA) and ensemble strategy can be adapted to detect fraud, anomalous API usage, or compromised service accounts in cloud environments.
• Reduced alert fatigue: By achieving >98 % precision, the system promises far fewer false alarms, allowing SOC analysts to focus on truly suspicious events.

Limitations & Future Work

• Synthetic dataset: The CERT data, while a standard benchmark, does not capture the full complexity of live enterprise logs (e.g., heterogeneous cloud services, encrypted traffic). Real‑world validation is needed.
• Feature engineering scope: The study relies on pre‑selected features; incorporating richer contextual signals (e.g., device posture, geolocation, workload patterns) could further improve detection.
• Model interpretability: AdaBoost ensembles are less transparent than rule‑based systems; future work could integrate explainable‑AI techniques to surface why a user is flagged.
• Adaptive adversaries: Insider attackers may deliberately mimic normal behavior to evade detection. Ongoing research into adversarial‑robust training and online learning would help keep the model ahead of evolving tactics.

Authors

• Gaurav Sarraf

Paper Information

• arXiv ID: 2601.06708v1
• Categories: cs.CR, cs.DC
• Published: January 10, 2026
• PDF: Download PDF