Intelligent Intrusion Detection Systems Using Hybrid Classification and Anomaly Detection Models

Sofia Bellini

Intelligent Intrusion Detection Systems Using Hybrid Classification and Anomaly Detection Models

Author(s)	Sofia Bellini
Country	Italy
Abstract	Intrusion detection systems analyze network or system activity to identify behavior that may indicate unauthorized or abnormal activity. Classification-based approaches can recognize patterns represented within labeled training data, whereas anomaly-detection methods attempt to identify observations that deviate from established normal behavior. Each approach has methodological limitations when used independently. Supervised classifiers may perform strongly on familiar patterns but depend on representative labels, while anomaly detectors may identify unusual activity without knowing whether every deviation is genuinely security relevant. This hypothetical methodological study examines an intelligent hybrid intrusion detection framework that combines supervised classification with anomaly detection. A fictional dataset containing 5,000 network-session observations is divided chronologically into training, validation, and testing partitions. Three conceptual systems are compared: random-forest classification, one-class support vector anomaly detection, and a hybrid architecture combining classification confidence with anomaly scoring. The study is limited to defensive analytical evaluation and provides no operational offensive procedures. The illustrative analysis evaluates accuracy, precision, recall, and F1 score using a hypothetical 800-observation test set containing 200 intrusion-labeled and 600 normal observations. Under the assigned values, the hybrid model records the strongest overall classification performance. A second comparison examines detection behavior across familiar intrusion patterns, previously unseen anomalous patterns, low-frequency suspicious behavior, and mixed traffic conditions. Performance varies across these conditions, demonstrating the importance of evaluating both known and unfamiliar events. The study concludes that combining supervised classification and anomaly detection provides a useful methodological framework for intelligent intrusion detection because the two approaches address complementary detection requirements. However, hybridization does not automatically guarantee reliable security performance. Practical effectiveness depends on representative traffic data, appropriate anomaly thresholds, control of false positives, temporally realistic testing, and continuous evaluation as network behavior changes.
Keywords	Intrusion Detection System, Hybrid Classification, Anomaly Detection, Machine Learning, Network Security, Random Forest, One-Class Support Vector Machine, Cybersecurity Analytics, Network Anomaly Detection, Intelligent Security Systems.
Field	Engineering
Published In	Volume 2, Issue 3, May-June 2026
Published On	2026-05-06

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Intelligent Intrusion Detection Systems Using Hybrid Classification and Anomaly Detection Models

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