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[2602.21252] INTACT: Intent-Aware Representation Learning for Cryptographic Traffic Violation Detection
Summary
The paper introduces INTACT, a novel framework for detecting cryptographic traffic violations by modeling violations as conditional constraints based on security intent, improving interpretability and robustness in anomaly detection.
Why It Matters
As cryptographic systems become increasingly complex, traditional anomaly detection methods fall short. INTACT addresses this gap by integrating security intent into violation detection, enhancing the effectiveness of monitoring systems and potentially improving cybersecurity measures.
Key Takeaways
- INTACT reformulates violation detection as conditional constraint learning.
- The framework uses intent conditioning to improve discrimination and interpretability.
- Evaluation shows INTACT achieves near-perfect discrimination in real-world datasets.
- The architecture separates behavioral and intent encoders for better performance.
- Results indicate significant improvements over traditional anomaly detection methods.
Computer Science > Cryptography and Security arXiv:2602.21252 (cs) [Submitted on 22 Feb 2026] Title:INTACT: Intent-Aware Representation Learning for Cryptographic Traffic Violation Detection Authors:Rahul D Ray View a PDF of the paper titled INTACT: Intent-Aware Representation Learning for Cryptographic Traffic Violation Detection, by Rahul D Ray View PDF HTML (experimental) Abstract:Security monitoring systems typically treat anomaly detection as identifying statistical deviations from observed data distributions. In cryptographic traffic analysis, however, violations are defined not by rarity but by explicit policy constraints, including key reuse prohibition, downgrade prevention, and bounded key lifetimes. This fundamental mismatch limits the interpretability and adaptability of conventional anomaly detection methods. We introduce INTACT (INTent-Aware Cryptographic Traffic), a policy-conditioned framework that reformulates violation detection as conditional constraint learning. Instead of learning a static decision boundary over behavioral features, INTACT models the probability of violation conditioned on both observed behavior and declared security intent. The architecture factorizes representation learning into behavioral and intent encoders whose fused embeddings produce a violation score, yielding a policy-parameterized family of decision boundaries. We evaluate the framework on a real-world network flow dataset and a 210,000-trace synthetic multi-intent cryptogra...
