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

arXiv: https://arxiv.org/abs/2511.05133 · HTML: https://mlsanity.com/paper/2511.05133/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2025.

## Built on by (most cited first, 3 of 3)

- [On Identifying Adversarial Intent Injection in AI-Native 6G Networks](https://mlsanity.com/paper/2609.12144/) (2026)
- [INTACT: Intent-Aware Representation Learning for Cryptographic Traffic Violation Detection](https://mlsanity.com/paper/2602.21252/) (2026)
- [SoK: Semantic Decision Engines in Network Control Loops](https://mlsanity.com/paper/2610.06425/) (2026)

## Abstract (arXiv)

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.