Zubair Md Fadlullah’s research while affiliated with Western University and other places

The open nature of wireless networks poses significant security challenges. Upper-layer authentication (ULA) methods face limitations, including vulnerability to cryptanaly-sis and replay attacks. Physical-layer authentication (PLA) has emerged as a promising technique to complement the ULA, offering robust two-factor authentication. PLA leverages physical-layer characteristics to authenticate transmitters with reduced complexity and latency. In this paper, we focus on passive PLA schemes for indoor Light Fidelity (LiFi) networks, where approximately 80% of data traffic originates indoors. Existing studies overlook channel impulse response (CIR) similarity under multiuser mobility and varying user densities, resulting in a lack of robust PLA solutions. To address this gap, we investigate CIR similarity in indoor LiFi environments. Our simulation results demonstrated that the CIR similarity is significant under multiuser mobility, where the attacker can share more than 30% of the CIR of a legitimate user. We propose a deep learning-based Long Short-Term Memory (LSTM) model that predicts the next CIR value based on historical data to enhance authentication reliability. Authentication is performed if the predicted CIR matches the actual CIR at the access point. Our numerical results show that the proposed LSTM-based PLA method effectively distinguishes legitimate users, achieving a detection probability exceeding 94% even under high multiuser mobility. Furthermore, the proposed LSTM-based PLA method can maintain the missed detection and the false alarm under 6% and 3% in the worst-case scenario, respectively.

Cell-free networks have emerged as a new paradigm for beyond-5G networks, offering uniform coverage and improved control over interference. However, scalability poses a challenge in full cell-free networks, where all access points (APs) serve all users. This challenge is addressed by user-centric clustering, where each user is served by a subset of APs, reducing complexity while maintaining coverage. In this paper, we provide an analysis of the relation between the user-centric clustering and pilot assignment problems in cell-free networks, and introduce a formulation which decouples both problems enabling each to be solved independently. We present a general problem formulation for the user-centric clustering problem, allowing the use of diverse per-user and network-wide performance metrics. Specifically, we focus on one instance of this framework, utilizing per-user spectral efficiency and network-wide sum spectral efficiency (SE) as metrics. Additionally, we formulate the pilot assignment problem to minimize overall channel estimation error while considering the user-centric clusters in evaluating the desirability of pilot assignments, which leads to better performing solutions. Both problems are classified as binary nonlinear programs that are at least NP-hard. To solve these optimization problems, our proposed methodology employs sample average approximation coupled with surrogate optimization for the user-centric clustering problem and utilizes the genetic algorithm for the pilot assignment problem. Numerical experiments demonstrate that the optimized solutions surpass baseline solutions, leading to significant improvements in spectral efficiency.

The development of high-quality deep learning models demands the transfer of user data from edge devices, where it originates, to centralized servers. This central training approach has scalability limitations and poses privacy risks to private data. Federated Learning (FL) is a distributed training framework that empowers physical smart systems devices to collaboratively learn a task without sharing private training data with a central server. However, FL introduces new challenges to Beyond 5G (B5G) networks, such as communication overhead, system heterogeneity, and privacy concerns, as the exchange of model updates may still lead to data leakage. This paper explores the communication overhead and privacy risks facing the implementation of FL and presents an algorithm that encompasses Knowledge Distillation (KD) and Differential Privacy (DP) techniques to address these challenges in FL. We compare the operational flow and network model of model-based and model-agnostic (KD-based) FL algorithms that enable customizing per-client model architecture to accommodate heterogeneous and constrained system resources. Our experiments show that KD-based FL algorithms are able to exceed local accuracy and achieve comparable accuracy to central training. Additionally, we show that applying DP to KDbased FL significantly damages its utility, leading to up to 70% accuracy loss for a privacy budget $\epsilon \leq 10$ .

5G technology has ushered in a new era of cellular networks characterized by unprecedented speeds and connectivity. However, these networks' dynamic and complex nature presents significant challenges in network management and Quality of Service (QoS) assurance. In this context, accurate throughput prediction is essential for optimizing network resources, improving traffic management, and enhancing user experiences. This study presents novel deep learning approaches utilizing Long Short-Term Memory (LSTM), Bi-directional LSTM (BiLSTM), and Artificial Neural Networks (ANN) to predict the throughput. The methodology achieves exceptional performance, surpassing existing methods. The motive behind leveraging deep learning algorithms is their exceptional ability to capture temporal dependencies and patterns within time-series data, which is intrinsic to network traffic. By employing these models, we can forecast network throughput with high precision, facilitating proactive resource allocation and congestion avoidance. Our approach maintains high QoS and supports cost efficiency and adaptive network maintenance. The BiLSTM and LSTM model's adaptability and learning capabilities make it well-suited for the ever-evolving 5G landscape, where user demands and network conditions fluctuate rapidly. This study demonstrates the technical feasibility and benefits of using BiLSTM and LSTM for overall throughput prediction. It highlights the broader implications for the future of 5G network management and optimization.

In the context of the growing proliferation of user devices and the concurrent surge in data volumes, the complexities arising from the substantial increase in data have posed formidable challenges to conventional machine learning model training. Particularly, this is evident within resource-constrained and security-sensitive environments such as those encountered in networks associated with the Internet of Things (IoT). Federated Learning has emerged as a promising remedy to these challenges by decentralizing model training to edge devices or parties, effectively addressing privacy concerns and resource limitations. Nevertheless, the presence of statistical heterogeneity in non-Independently and Identically Distributed (non-IID) data across different parties poses a significant hurdle to the effectiveness of FL. Many FL approaches have been proposed to enhance learning effectiveness under statistical heterogeneity. However, prior studies have uncovered a gap in the existing research land-scape, particularly in the absence of a comprehensive comparison between federated methods addressing statistical heterogeneity in detecting IoT attacks. In this research endeavor, we delve into the exploration of FL algorithms, specifically FedAvg, FedProx, and Scaffold, under different data distributions. Our focus is on achieving a comprehensive understanding of and addressing the challenges posed by statistical heterogeneity. In this study, We classify large-scale IoT attacks by utilizing the CICIoT2023dataset. Through meticulous analysis and experimentation, our objective is to illuminate the performance nuances of these FL methods, providing valuable insights for researchers and practitioners in the domain.