Data Access Anomaly Detection using Sequence Models

Computer system growth has created greater demand for robust security measures that can detect anomalous data access patterns. This paper is a comprehensive discussion of sequence-based anomaly detection techniques on organizational data access logs. A test dataset of 5,500 access records with 12 features was employed to identify patterns that indicate potential security breaches or policy violations. Three machine learning models, Random Forest, Support Vector Machine, and XGBoost, were compared, with respective accuracies of 90.18%, 92.73%, and 91.36%. As is evident from the study, sequence models can effectively learn temporal dependencies in user behavior, making it possible for early anomaly detection. Feature engineering that considers user tenure, frequency of access patterns, and resource sensitivity scores was also critical in determining the model performance. The outcomes show that the ensemble methods combined with temporal sequence analysis have higher anomaly detection capability compared to traditional static methods. The proposed methodology has actionable implications for enterprise security systems, particularly for applications requiring real-time monitoring of data access patterns.