Detection of Misreporting Attacks on Software-Defined Immersive Environments

2025-09-22

AI Summary

To address false-reporting attacks and service degradation caused by spurious load reporting from switches in software-defined immersive environments, this paper proposes a hybrid machine learning framework integrating temporal anomaly detection and supervised classification. The framework models the temporal dependencies inherent in genuine load reports and jointly leverages unsupervised anomaly scoring—capable of detecting subtle deviations—and supervised binary classification—designed to discern malicious intent—thereby significantly enhancing early detection of dynamic, low-intensity false-reporting attacks. Evaluated on real-world testbed data, the method achieves >96% recall and <2% false negative rate across multiple attack scenarios, improving detection robustness by 12.7% on average over baseline models. This advancement effectively safeguards service quality stability for immersive applications.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Machine Learning: Hardware-aware ML
• Computer Vision: Adversarial Attacks & Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Detecting stealthy misreporting attacks causing load imbalance in SDN
• Preventing quality degradation in software-defined immersive environments
• Identifying temporal inconsistencies in switch-reported loads using ML

Innovation

• Hybrid machine learning for anomaly detection
• Combines unsupervised scoring with supervised classification
• Captures temporal inconsistencies in switch loads