Vahid Shah-Mansouri

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.

Salar Nouri, Mojdeh Karbalaee Motalleb, Vahid Shah-Mansouri et al.

This paper introduces a novel generative AI (GAI)-driven, unified semi-supervised learning architecture for optimizing resource allocation and network slicing in O-RAN. Termed Generative Semi-Supervised VAE-Contrastive Learning, our approach maximizes the weighted user equipment (UE) throughput and allocates physical resource blocks (PRBs) to enhance the quality of service for eMBB and URLLC services. The GAI framework utilizes a dedicated xApp for intelligent power control and PRB allocation. This integrated GAI model synergistically combines the generative power of a VAE with contrastive learning to achieve robustness in an end-to-end trainable system. It is a semi-supervised training approach that concurrently optimizes supervised regression of resource allocation decisions (i.e., power, UE association, PRB) and unsupervised contrastive objectives. This intrinsic fusion improves the precision of resource management and model generalization in dynamic mobile networks. We evaluated our GAI methodology against exhaustive search and deep Q-Network algorithms using key performance metrics. Results show our integrated GAI approach offers superior efficiency and effectiveness in various scenarios, presenting a compelling GAI-based solution for critical network slicing and resource management challenges in next-generation O-RAN systems.

Hojjat Navidan, Parisa Fard Moshiri, Mohammad Nabati et al.

Despite the recency of their conception, Generative Adversarial Networks (GANs) constitute an extensively researched machine learning sub-field for the creation of synthetic data through deep generative modeling. GANs have consequently been applied in a number of domains, most notably computer vision, in which they are typically used to generate or transform synthetic images. Given their relative ease of use, it is therefore natural that researchers in the field of networking (which has seen extensive application of deep learning methods) should take an interest in GAN-based approaches. The need for a comprehensive survey of such activity is therefore urgent. In this paper, we demonstrate how this branch of machine learning can benefit multiple aspects of computer and communication networks, including mobile networks, network analysis, internet of things, physical layer, and cybersecurity. In doing so, we shall provide a novel evaluation framework for comparing the performance of different models in non-image applications, applying this to a number of reference network datasets.

Ali Parchekani, Salar Nouri, Vahid Shah-Mansouri et al.

In this paper, we introduce a novel end-to-end traffic classification method to distinguish between traffic classes including VPN traffic in three layers of the Open Systems Interconnection (OSI) model. Classification of VPN traffic is not trivial using traditional classification approaches due to its encrypted nature. We utilize two well-known neural networks, namely multi-layer perceptron and recurrent neural network to create our cascade neural network focused on two metrics: class scores and distance from the center of the classes. Such approach combines extraction, selection, and classification functionality into a single end-to-end system to systematically learn the non-linear relationship between input and predicted performance. Therefore, we could distinguish VPN traffics from non-VPN traffics by rejecting the unrelated features of the VPN class. Moreover, we obtain the application type of non-VPN traffics at the same time. The approach is evaluated using the general traffic dataset ISCX VPN-nonVPN, and an acquired dataset. The results demonstrate the efficacy of the framework approach for encrypting traffic classification while also achieving extreme accuracy, $95$ percent, which is higher than the accuracy of the state-of-the-art models, and strong generalization capabilities.