Cross-Domain Adversarial Alignment for Network Anomaly Detection Through Behavioral Embedding Enrichment

Detecting anomalies in network traffic is a central task in cybersecurity and digital infrastructure management. Traditional approaches rely on statistical models, rule-based systems, or machine learning techniques to identify deviations from expected patterns, but often face limitations in generalization across domains. This study proposes a cross-domain data enrichment framework that integrates behavioral embeddings with network traffic features through adversarial autoencoders. Each network traffic record is paired with the most similar behavioral profile embedding from user web activity data (Charles dataset) using cosine similarity, thereby providing contextual enrichment for anomaly detection. The proposed system comprises (i) behavioral profile clustering via autoencoder embeddings and (ii) cross-domain latent alignment through adversarial autoencoders, with a discriminator to enable feature fusion. A Deep Feedforward Neural Network trained on the enriched feature space achieves 97.17% accuracy, 96.95% precision, 97.34% recall, and 97.14% F1-score, with stable cross-validation performance (99.79% average accuracy across folds). Behavioral clustering quality is supported by a silhouette score of 0.86 and a Davies–Bouldin index of 0.57. To assess robustness and transferability, the framework was evaluated on the UNSW-NB15 and the CIC-IDS2017 datasets, where results confirmed consistent performance and reliability when compared to traffic-only baselines. This supports the feasibility of cross-domain alignment and shows that adversarial training enables stable feature integration without evidence of overfitting or memorization.