Intrusion Detection with Anomaly Representation Learning and Semantic Grounding across Enterprise Network Traffic

Abstract

The proliferation of sophisticated cyber threats within enterprise network environments has necessitated the deployment of advanced intrusion detection systems. While contemporary deep learning methodologies exhibit remarkable detection capabilities, their inherent opacity creates a significant semantic gap between statistical anomaly detection and human-interpretable security intelligence. This paper presents a comprehensive framework for explaining intrusion detection through the synergistic integration of anomaly representation learning and semantic grounding. By transforming raw, high-dimensional network traffic features into a structured latent space, the proposed methodology captures the underlying distributions of normal enterprise behavior. Subsequently, a semantic grounding mechanism maps these opaque latent representations onto a predefined ontology of human-understandable network concepts, enabling the automatic generation of transparent, context-aware explanations for identified anomalies. The architectural design leverages a dual-phase training paradigm, wherein unsupervised contrastive learning is first utilized to isolate anomalous vectors, followed by an alignment process that associates specific vector dimensions with semantic attributes such as irregular payload structures or anomalous temporal flow patterns. Extensive theoretical analysis and conceptual evaluation indicate that bridging the semantic gap not only enhances the trustworthiness of automated security systems but also substantially reduces the cognitive load imposed on security analysts during incident response. This research contributes a foundational paradigm shift in cybersecurity, moving beyond mere threat detection toward holistic, interpretable threat comprehension.