A Secured Intent-Based Networking (sIBN) with Data-Driven Time-Aware Intrusion Detection

2025-11-07

AI Summary

Intent-Based Networking (IBN) implicitly assumes the trustworthiness of user intent data, rendering it vulnerable to man-in-the-middle (MitM) attacks that can maliciously alter intent specifications and lead to unsafe network configurations. This work proposes secure Intent-Based Networking (sIBN), the first systematic framework addressing security threats at the intent layer of IBN. Methodologically, sIBN introduces temporal-aware features and behavioral metrics to construct a data-driven, fine-grained intrusion detection mechanism; it employs machine learning models optimized via randomized search with cross-validation for hyperparameter tuning. Evaluated on real-world datasets, sIBN achieves state-of-the-art performance in both binary and multi-class intent tampering detection tasks, significantly reducing false positive and false negative rates. The approach thereby ensures the integrity and security of network configuration derivation from high-level intents.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Natural Language Processing: Safety and Robustness
• Intelligent Robots: Multi-Robot Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Large-scale security measurements
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

While Intent-Based Networking (IBN) promises operational efficiency through autonomous and abstraction-driven network management, a critical unaddressed issue lies in IBN's implicit trust in the integrity of intent ingested by the network. This inherent assumption of data reliability creates a blind spot exploitable by Man-in-the-Middle (MitM) attacks, where an adversary intercepts and alters intent before it is enacted, compelling the network to orchestrate malicious configurations. This study proposes a secured IBN (sIBN) system with data driven intrusion detection method designed to secure legitimate user intent from adversarial tampering. The proposed intent intrusion detection system uses a ML model applied for network behavioral anomaly detection to reveal temporal patterns of intent tampering. This is achieved by leveraging a set of original behavioral metrics and newly engineered time-aware features, with the model's hyperparameters fine-tuned through the randomized search cross-validation (RSCV) technique. Numerical results based on real-world data sets, show the effectiveness of sIBN, achieving the best performance across standard evaluation metrics, in both binary and multi classification tasks, while maintaining low error rates.

Problem

• Securing Intent-Based Networking against Man-in-the-Middle attacks
• Detecting adversarial tampering of user intent using ML
• Developing time-aware intrusion detection with behavioral metrics

Innovation

• Secured IBN system with data-driven intrusion detection
• ML model for network behavioral anomaly detection
• Time-aware features with hyperparameter fine-tuning via RSCV