Chafika Benzaïd

Somayeh Kianpisheh, Chafika Benzaid, Tarik Taleb

Federated Learning (FL) enables training of a global model from distributed data, while preserving data privacy. However, the singular-model based operation of FL is open with uploading poisoned models compatible with the global model structure and can be exploited as a vulnerability to conduct model poisoning attacks. This paper proposes a multi-model based FL as a proactive mechanism to enhance the opportunity of model poisoning attack mitigation. A master model is trained by a set of slave models. To enhance the opportunity of attack mitigation, the structure of client models dynamically change within learning epochs, and the supporter FL protocol is provided. For a MEC system, the model selection problem is modeled as an optimization to minimize loss and recognition time, while meeting a robustness confidence. In adaption with dynamic network condition, a deep reinforcement learning based model selection is proposed. For a DDoS attack detection scenario, results illustrate a competitive accuracy gain under poisoning attack with the scenario that the system is without attack, and also a potential of recognition time improvement.

Samira Kamali Poorazad, Chafika Benzaïd, Tarik Taleb

In the light of the growing connectivity and sensitivity of industrial data, cyberattacks and data breaches are becoming more common in the Industrial Internet of Things (IIoT). To cope with such threats, this study presents an anomaly detection system based on a novel Federated Learning (FL) framework. This system detects anomalies such as cyberattacks and protects industrial data privacy by processing data locally and training anomaly detection models on industrial agents without sharing raw data. The proposed FL framework incorporates two key components to enhance both privacy and efficiency. The first component is Homomorphic Encryption (HE), which is integrated into the framework to further protect sensitive data transmissions such as model parameters. HE enhances privacy in FL by preventing adversaries from inferring private industrial data through attacks, such as model inversion attacks. The second component is an innovative dynamic agent selection scheme, wherein a selection threshold is calculated based on agent delays and data size. The purpose of this new scheme is to mitigate the straggler effect and the communication bottleneck that occur in traditional FL architectures, such as synchronous and asynchronous architectures. It ensures that agents are not unfairly selected by the different delays resulting from heterogeneous data in IIoT environments, while simultaneously improving model performance and convergence speed. The proposed framework exhibits superior performance over baseline approaches in terms of accuracy, precision, F1-scores, communication costs, convergence speeds, and fairness rate.

Mojdeh Karbalaee Motalleb, Chafika Benzaid, Tarik Taleb et al.

The evolution of wireless communication systems will be fundamentally impacted by an open radio access network (O-RAN), a new concept defining an intelligent architecture with enhanced flexibility, openness, and the ability to slice services more efficiently. For all its promises, and like any technological advancement, O-RAN is not without risks that need to be carefully assessed and properly addressed to accelerate its wide adoption in future mobile networks. In this paper, we present an in-depth security analysis of the O-RAN architecture, discussing the potential threats that may arise in the different O-RAN architecture layers and their impact on the Confidentiality, Integrity, and Availability (CIA) triad. We also promote the potential of zero trust, Moving Target Defense (MTD), blockchain, and large language models(LLM) technologies in fortifying O-RAN's security posture. Furthermore, we numerically demonstrate the effectiveness of MTD in empowering robust deep reinforcement learning methods for dynamic network slice admission control in the O-RAN architecture. Moreover, we examine the effect of explainable AI (XAI) based on LLMs in securing the system.

C. Benzaid, T. Taleb

Artificial Intelligence (AI) is envisioned to play a pivotal role in empowering intelligent, adaptive and autonomous security management in 5G and beyond networks, thanks to its potential to uncover hidden patterns from a large set of time-varying multi-dimensional data, and deliver faster and accurate decisions. Unfortunately, AI's capabilities and vulnerabilities make it a double-edged sword that may jeopardize the security of future networks. This paper sheds light on how AI may impact the security of 5G and its successive from its posture of defender, offender or victim, and recommends potential defenses to safeguard from malevolent AI while pointing out their limitations and adoption challenges.

Y. Dang, C. Benzaid, B. Yang et al.

Cellular-based Unmanned Aerial Vehicle (UAV) systems are a promising paradigm to provide reliable and fast Beyond Visual Line of Sight (BVLoS) communication services for UAV operations. However, such systems are facing a serious GPS spoofing threat for UAV's position. To enable safe and secure UAV navigation BVLoS, this paper proposes a cellular network assisted UAV position monitoring and anti-GPS spoofing system, where deep learning approach is used to live detect spoofed GPS positions. Specifically, the proposed system introduces a MultiLayer Perceptron (MLP) model which is trained on the statistical properties of path loss measurements collected from nearby base stations to decide the authenticity of the GPS position. Experiment results indicate the accuracy rate of detecting GPS spoofing under our proposed approach is more than 93% with three base stations and it can also reach 80% with only one base station.