Fake Traffic Injection Attacks and Global-Local Inconsistency Detection for Securing Federated Learning-based Wireless Traffic Prediction

Core Concepts

Federated learning offers a distributed framework to train a global model across multiple base stations without compromising the privacy of their local network data. However, the security aspects of FL-based distributed wireless systems, particularly in regression-based wireless traffic prediction (WTP) problems, remain inadequately investigated. This work introduces a novel fake traffic injection (FTI) attack to undermine the FL-based WTP system by injecting fabricated traffic distributions with minimal knowledge. It also proposes a defense mechanism, termed global-local inconsistency detection (GLID), which strategically removes abnormal model parameters to mitigate the impact of model poisoning attacks on WTP.

Abstract

The content discusses the security challenges in federated learning-based wireless traffic prediction (WTP) systems. It introduces a novel attack strategy called Fake Traffic Injection (FTI) that aims to undermine the integrity of the FL-based WTP system by injecting fabricated traffic distributions from fake base stations (BSs) with minimal knowledge.
The key highlights are:

Federated learning (FL) offers a distributed framework to train a global model across multiple BSs without compromising the privacy of their local network data, making it ideal for WTP applications.
However, the security aspects of FL-based distributed wireless systems, particularly in regression-based WTP problems, remain inadequately investigated.
The FTI attack is designed to create undetectable fake BSs that employ both their initial model and current global information to determine the optimizing trajectory of the FL process on WTP, aiming to subtly align the global model towards an outcome that undermines the integrity and reliability of the data learning process.
The paper also proposes a defense mechanism called Global-Local Inconsistency Detection (GLID), which strategically removes abnormal model parameters that deviate beyond a specific percentile range estimated through statistical methods in each dimension.
Extensive evaluations on real-world wireless traffic datasets demonstrate that the FTI attack significantly compromises FL-based WTP systems, while the GLID defense mechanism substantially mitigates the impact of model poisoning attacks.

Stats

The wireless traffic data in Milan is segmented into 10,000 grid cells, with each cell served by a base station covering an area of approximately 235 meters on each side.

Quotes

"Federated learning (FL) represents an evolving paradigm in distributed machine learning techniques, allowing a unified model to be trained across numerous devices containing local data samples, all without the need to transmit these samples to a central server."
"Particularly, in the era of 5G and beyond, where technologies like network slicing and edge computing play crucial roles, WTP becomes essential for optimizing these advancements, which not only enhances user experience but also facilitates the provision of innovative services that demand high bandwidth and low latency."
"To bridge this gap, we make the first attempt to introduce a novel attack centered on injecting fake base station (BS) traffic into wireless networks."

Key Insights Distilled From

Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction

by Zifan Zhang,... at arxiv.org 04-23-2024

https://arxiv.org/pdf/2404.14389.pdf

Deeper Inquiries

How can the proposed FTI attack be extended to target other types of distributed machine learning systems beyond wireless traffic prediction

The FTI attack can be extended to target other types of distributed machine learning systems by adapting the methodology to suit the specific characteristics of the target system. For instance, in a scenario where the distributed machine learning system is used for anomaly detection in IoT devices, the FTI attack could be modified to inject fake anomaly data into the training process. By crafting malicious data points that deviate from normal patterns, the attacker can manipulate the global model to produce inaccurate anomaly predictions. Similarly, in a distributed recommendation system, the FTI attack could involve injecting biased user preferences to skew the recommendations provided by the system. By understanding the data distribution and model aggregation process of the target system, the attacker can strategically inject fake data to undermine the overall performance and reliability of the system.

What are the potential countermeasures that network operators can implement to further strengthen the resilience of FL-based systems against model poisoning attacks like FTI

Network operators can implement several countermeasures to strengthen the resilience of FL-based systems against model poisoning attacks like FTI. Some potential strategies include:

Data Sanitization: Implementing rigorous data validation and sanitization processes to detect and filter out anomalous or malicious data before it is used for training the global model.
Model Robustness: Employing robust model aggregation techniques that are resilient to adversarial attacks, such as Byzantine-robust aggregation rules like Krum or Trim, to mitigate the impact of compromised BSs.
Anomaly Detection: Integrating anomaly detection mechanisms within the FL system to identify unusual behavior or model deviations that may indicate a poisoning attack.
Regular Monitoring: Continuously monitoring the performance metrics of the FL system to detect any sudden changes or anomalies that could be indicative of a security breach.
Secure Communication: Implementing secure communication protocols and encryption mechanisms to protect the transmission of data and model updates between BSs and the central server.
Dynamic Parameter Adjustment: Adapting the parameters of the defense mechanisms, such as the percentile range in GLID, based on real-time feedback and performance evaluation to enhance the system's adaptability to evolving threats.

By implementing a combination of these countermeasures, network operators can enhance the security and resilience of FL-based systems against model poisoning attacks and ensure the integrity of the distributed machine learning process.

What are the broader implications of secure and reliable wireless traffic prediction in the context of emerging technologies like autonomous vehicles, smart cities, and industrial automation

Secure and reliable wireless traffic prediction has significant implications for various emerging technologies, including autonomous vehicles, smart cities, and industrial automation:

Autonomous Vehicles: Accurate wireless traffic prediction is crucial for enabling efficient communication and coordination among autonomous vehicles on the road. Reliable predictions help in optimizing traffic flow, reducing congestion, and enhancing safety by providing real-time information about road conditions and traffic patterns.
Smart Cities: In smart city environments, wireless traffic prediction plays a key role in managing urban infrastructure, optimizing resource allocation, and improving overall city operations. By predicting traffic patterns and demand for services, smart cities can enhance transportation systems, energy efficiency, and public safety.
Industrial Automation: In industrial automation settings, reliable wireless traffic prediction enables efficient communication and coordination among interconnected devices and systems. Predicting traffic loads and network demands helps in optimizing production processes, minimizing downtime, and enhancing overall operational efficiency in industrial settings.

By leveraging secure and reliable wireless traffic prediction in these contexts, organizations can improve decision-making, enhance operational efficiency, and drive innovation in various sectors that rely on seamless and efficient communication networks.