Anomaly Detection in Intranet File Distribution Systems Using Machine Learning Algorithms
International Research Journal of Modernization in Engineering Technology and Science · 2025
This study investigates the application of machine learning (ML) algorithms for anomaly detection in such environments, addressing the limitations of traditional rule-based methods that often fail to adapt to evolving threats.In the absence of publicly available datasets, a synthetic dataset was created to simulate both normal and anomalous file transfer activities.Three ML algorithms were evaluated: Isolation Forest, One-Class Support Vector Machine (SVM), and Random Forest, using metrics such as accuracy, precision, recall, F1-score, and AUC-ROC.Results show that Random Forest achieved the highest performance with 97.5% accuracy and strong generalization, making it ideal for environments with labeled data.Isolation Forest demonstrated robust detection of unseen anomalies in unlabeled or privacy-sensitive contexts.One-Class SVM underperformed, largely due to parameter sensitivity and lower accuracy.These findings underscore the viability of both supervised and unsupervised ML models in detecting intranet-based file anomalies.The study concludes with recommendations for model selection based on data availability and privacy considerations, and proposes future work on real-time detection and deployment in live enterprise networks.