# Detection of Misreporting Attacks on Software-Defined Immersive Environments

arXiv: https://arxiv.org/abs/2509.18040 · HTML: https://mlsanity.com/paper/2509.18040/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 31 references that are in the map.
- First posted on arXiv 22 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [Detection and Recovery of Adversarial Slow-Pose Drift in Offloaded Visual-Inertial Odometry](https://mlsanity.com/paper/2509.07130/) (2025)
- [Manipulating OpenFlow Link Discovery Packet Forwarding for Topology Poisoning](https://mlsanity.com/paper/2408.16940/) (2024)
- [Impact of Error Rate Misreporting on Resource Allocation in Multi-tenant Quantum Computing and Defense](https://mlsanity.com/paper/2504.04285/) (2025)

## Built on by (most cited first, 2 of 2)

- [Noise-Aware Misclassification Attack Detection in Collaborative DNN Inference](https://mlsanity.com/paper/2603.17914/) (2026)
- [SDNGuardStack: An Explainable Ensemble Learning Framework for High-Accuracy Intrusion Detection in Software-Defined Networks](https://mlsanity.com/paper/2604.20934/) (2026)

## Abstract (arXiv)

The ability to centrally control network infrastructure using a programmable middleware has made Software-Defined Networking (SDN) ideal for emerging applications, such as immersive environments. However, such flexibility introduces new vulnerabilities, such as switch misreporting led load imbalance, which in turn make such immersive environment vulnerable to severe quality degradation. In this paper, we present a hybrid machine learning (ML)-based network anomaly detection framework that identifies such stealthy misreporting by capturing temporal inconsistencies in switch-reported loads, and thereby counter potentially catastrophic quality degradation of hosted immersive application. The detection system combines unsupervised anomaly scoring with supervised classification to robustly distinguish malicious behavior. Data collected from a realistic testbed deployment under both benign and adversarial conditions is used to train and evaluate the model. Experimental results show that the framework achieves high recall in detecting misreporting behavior, making it effective for early and reliable detection in SDN environments.