Enhancing a Multi-Lens Behavioral Anomaly-Detection Framework for Insider Threat Mitigation
TL;DR
It is indicated that a multi-lens deep learning framework can effectively detect insider threats with high accuracy, low false positive rates, and improved interpretability, and results indicate that combining multiple behavioral lenses yields more robust and practically useful alerts than single-view or solely sequence-based methods.
Abstract
The growing challenges posed by insider attacks cannot be addressed by traditional defense mechanisms. This study addresses these challenges by proposing a behavioral anomaly detection framework that examines user activities from four complementary perspectives: aggregate daily actions, temporal event sequences, group sociability patterns, and deviations from data-driven role/peer baselines. The framework employs machine learning models, including Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) predictors, as well as autoencoders, to forecast event sequences and evaluate deviations from data-driven role/peer prototypes learned from historical normal behavior. The prediction mechanism captures static action counts and social interactions and incorporates daily deviation signals through a multilayer perceptron. The resulting decision logic is approximated using an interpretable surrogate decision tree. The framework achieves an overall accuracy of 96.7%, detects 90% of anomalies with a false-positive rate of 2.7%, and provides an early warning lead time of over 3 days before harmful activity manifests. Compared to prior multi-model baselines, the F1 score improves by 5–10 percentage points. Empirical evaluations on the CERT insider threat dataset show that the proposed framework achieves high detection accuracy with low false-positive rates. Results indicate that the average early warning lead time exceeds 3 days before harmful activity occurs. In particular, the fusion model reaches an overall accuracy of about 96.7%, detects roughly 90% of true anomalies at a false positive rate of around 2.7%, and improves the F1 score by approximately 5-10% compared to state-of-the-art baselines. Such results indicate that combining multiple behavioral lenses yields more robust and practically useful alerts than single-view or solely sequence-based methods. This study indicates that a multi-lens deep learning framework can effectively detect insider threats with high accuracy, low false positive rates, and improved interpretability. The proposed methodology provides a practical solution for deployment in organizational security operations.