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

• URL: http://arxiv.org/abs/2511.05133v1
• Date: Fri, 07 Nov 2025 10:28:01 GMT
• Title: A Secured Intent-Based Networking (sIBN) with Data-Driven Time-Aware Intrusion Detection
• Authors: Urslla Uchechi Izuazu, Mounir Bensalem, Admela Jukan,
• Abstract summary: This study proposes a secured IBN (sIBN) system with data driven intrusion detection method designed to secure user intent from adversarial tampering.<n>The proposed intent intrusion detection system uses a ML model applied for network behavioral anomaly detection to reveal temporal patterns of intent tampering.
• Score: 2.7273279761148967
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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