Securing Distributed Network Digital Twin Systems Against Model Poisoning Attacks

2024-07-02

IEEE Internet of Things Journal

AI Summary

Digital twin (NDT) systems for 5G+ networks face security vulnerabilities in wireless traffic forecasting, particularly susceptibility to model poisoning attacks. Method: This paper proposes the first lightweight forged-traffic injection attack paradigm that requires neither prior knowledge nor access to original training data. To counter such attacks, we design a Global-Local Inconsistency Detection (GLID) defense framework featuring robust federated aggregation via parameter-distribution percentile truncation. GLID integrates time-series modeling, anomalous parameter detection, and quantile-based threshold clipping. Contribution/Results: Evaluated on real-world wireless traffic datasets, our attack achieves a 23.6% higher success rate than state-of-the-art methods. Under GLID defense, post-poisoning prediction error is reduced to one-fifth of the baseline, significantly enhancing the robustness and trustworthiness of NDT systems.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

In the era of 5G and beyond, the increasing complexity of wireless networks necessitates innovative frameworks for efficient management and deployment. Digital twins (DTs), embodying real-time monitoring, predictive configurations, and enhanced decision-making capabilities, stand out as a promising solution in this context. Within a time-series data-driven framework that effectively maps wireless networks into digital counterparts, encapsulated by integrated vertical and horizontal twinning phases, this study investigates the security challenges in distributed network DT (NDT) systems, which potentially undermine the reliability of subsequent network applications, such as wireless traffic forecasting. Specifically, we consider a minimal-knowledge scenario for all attackers, in that they do not have access to network data and other specialized knowledge, yet can interact with previous iterations of server-level models. In this context, we spotlight a novel fake traffic injection attack designed to compromise a distributed NDT system for wireless traffic prediction. In response, we then propose a defense mechanism, termed global-local inconsistency detection (GLID), to counteract various model poisoning threats. GLID strategically removes abnormal model parameters that deviate beyond a particular percentile range, thereby fortifying the security of network twinning process. Through extensive experiments on real-world wireless traffic data sets, our experimental evaluations show that both our attack and defense strategies significantly outperform existing baselines, highlighting the importance of security measures in the design and implementation of DTs for 5G and beyond network systems.

Problem

• Distributed Network
• Model Poisoning Attack
• Digital Twin System Protection

Innovation

• GLID
• Anomaly Detection
• Model Poisoning Defense