arXiv subjects

Bechir Hamdaoui

Publications and source records attributed to Bechir Hamdaoui.

At least 19 recordsLinked to original sources

arXiv2026

Characterizing and Mitigating the Effects of Device Temperature on RF Fingerprinting Accuracy

Radio Frequency Fingerprinting (RFFP) has emerged as a promising approach for device authentication by exploiting hardware-specific impairments embedded in transmitted signals. Yet existing methods largely overlook a major drawback: RFFP sensitivity to temperature--a critical factor influenced by both internal and environmental conditions--which can significantly alter device signatures and degrade classification performance. In this paper, we propose a novel temperature-aware RFFP framework that explicitly incorporates device temperature information into the learning process to improve robustness and generalization. We evaluate the proposed method on a real-world Bluetooth Low Energy (BLE) dataset collected across multiple devices and environmental conditions. Experimental results demonstrate that temperature-aware modeling consistently outperforms other temperature mitigation baselines, achieving significant improvements in classification accuracy, particularly under unseen temperature and environmental conditions.

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arXiv2026

Replicating the Signature: Unsupervised Targeted Impersonation Attack on RF Fingerprinting

This paper presents a novel impersonation attack framework that aims to fool RF Fingerprinting (RFFP) identification systems by synthesizing signals that replicate the hardware-specific impairments of a target device. Our framework leverages unsupervised learning to enable accurate impairment estimation, combined with signal processing-based generation to synthesize high-fidelity adversarial signals. Unlike prior works that assume full access to the legitimate (victim) RFFP classifier, we consider a more realistic attack strategy where the adversary performs the attack from a completely different transceiver hardware. We further evaluate our proposed attack under realistic and challenging deployment settings, including over-the-air transmission in both Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) scenarios. Extensive experiments conducted on a Bluetooth Low Energy (BLE) device testbed demonstrate that our attacks remain highly effective even under severe access constraints, significantly outperforming existing baselines in terms of targeted attack success rates by over 80%. We additionally analyze the effects of cross-domain generalization, signal representation mismatch, and classifier diversity, highlighting the robustness and

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arXiv2026

HEEDFUL: Leveraging Sequential Transfer Learning for Robust WiFi Device Fingerprinting Amid Hardware Warm-Up Effects

Deep Learning-based RF fingerprinting approaches struggle to perform well in cross-domain scenarios, particularly during hardware warm-up. This often-overlooked vulnerability has been jeopardizing their reliability and their adoption in practical settings. To address this critical gap, in this work, we first dive deep into the anatomy of RF fingerprints, revealing insights into the temporal fingerprinting variations during and post hardware stabilization. Introducing HEEDFUL, a novel framework harnessing sequential transfer learning and targeted impairment estimation, we then address these challenges with remarkable consistency, eliminating blind spots even during challenging warm-up phases. Our evaluation showcases HEEDFUL's efficacy, achieving remarkable classification accuracies of up to 96% during the initial device operation intervals-far surpassing traditional models. Furthermore, cross-day and cross-protocol assessments confirm HEEDFUL's superiority, achieving and maintaining high accuracy during both the stable and initial warm-up phases when tested on WiFi signals. Additionally, we release WiFi type B and N RF fingerprint datasets that, for the first time, incorporate both the time-domain representation and real hardware impairments of the frames. This underscores the importance of leveraging hardware impairment data, enabling a deeper understanding of fingerprints and facilitating the development of more robust RF fingerprinting solutions.

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arXiv2025

Bluetooth Fingerprint Identification Under Domain Shift Through Transient Phase Derivative

Deep learning-based radio frequency fingerprinting (RFFP) has become an enabling physical-layer security technology, allowing device identification and authentication through received RF signals. This technology, however, faces significant challenges when it comes to adapting to domain variations, such as time, location, environment, receiver and channel. For Bluetooth Low Energy (BLE) devices, addressing these challenges is particularly crucial due to the BLE protocol's frequency-hopping nature. In this work, and for the first time, we investigated the frequency hopping effect on RFFP of BLE devices, and proposed a novel, low-cost, domain-adaptive feature extraction method. Our approach improves the classification accuracy by up to 58\% across environments and up to 80\% across receivers compared to existing benchmarks.

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arXiv2025

Secret-Key Agreement Through Hidden Markov Modeling of Wavelet Scattering Embeddings

Secret-key generation and agreement based on wireless channel reciprocity offers a promising avenue for securing IoT networks. However, existing approaches predominantly rely on the similarity of instantaneous channel measurement samples between communicating devices. This narrow view of reciprocity is often impractical, as it is highly susceptible to noise, asynchronous sampling, channel fading, and other system-level imperfections -- all of which significantly impair key generation performance. Furthermore, the quantization step common in traditional schemes introduces irreversible errors, further limiting efficiency. In this work, we propose a novel approach for secret-key generation by using wavelet scattering networks to extract robust and reciprocal CSI features. Dimensionality reduction is applied to uncover hidden cluster structures, which are then used to build hidden Markov models for efficient key agreement. Our approach eliminates the need for quantization and effectively captures channel randomness. It achieves a 5x improvement in key generation rate compared to traditional benchmarks, providing a secure and efficient solution for key generation in resource-constrained IoT environments.

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arXiv2025

Wavelet-Based CSI Reconstruction for Improved Wireless Security Through Channel Reciprocity

The reciprocity of channel state information (CSI) collected by two devices communicating over a wireless channel has been leveraged to provide security solutions to resource-limited IoT devices. Despite the extensive research that has been done on this topic, much of the focus has been on theoretical and simulation analysis. However, these security solutions face key implementation challenges, mostly pertaining to limitations of IoT hardware and variations of channel conditions, limiting their practical adoption. To address this research gap, we revisit the channel reciprocity assumption from an experimental standpoint using resource-constrained devices. Our experimental study reveals a significant degradation in channel reciprocity for low-cost devices due to the varying channel conditions. Through experimental investigations, we first identify key practical causes for the degraded channel reciprocity. We then propose a new wavelet-based CSI reconstruction technique using wavelet coherence and time-lagged cross-correlation to construct CSI data that are consistent between the two participating devices, resulting in significant improvement in channel reciprocity. Additionally, we propose a secret-key generation scheme that exploits the wavelet-based CSI reconstruction, yielding significant increase in the key generation rates. Finally, we propose a technique that exploits CSI temporal variations to enhance device authentication resiliency through effective detection of replay attacks.

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arXiv2024

Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift

Radio Frequency (RF) device fingerprinting has been recognized as a potential technology for enabling automated wireless device identification and classification. However, it faces a key challenge due to the domain shift that could arise from variations in the channel conditions and environmental settings, potentially degrading the accuracy of RF-based device classification when testing and training data is collected in different domains. This paper introduces a novel solution that leverages contrastive learning to mitigate this domain shift problem. Contrastive learning, a state-of-the-art self-supervised learning approach from deep learning, learns a distance metric such that positive pairs are closer (i.e. more similar) in the learned metric space than negative pairs. When applied to RF fingerprinting, our model treats RF signals from the same transmission as positive pairs and those from different transmissions as negative pairs. Through experiments on wireless and wired RF datasets collected over several days, we demonstrate that our contrastive learning approach captures domain-invariant features, diminishing the effects of domain-specific variations. Our results show large and consistent improvements in accuracy (10.8\% to 27.8\%) over baseline models, thus underscoring the effectiveness of contrastive learning in improving device classification under domain shift.

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arXiv2024

On the Domain Generalizability of RF Fingerprints Through Multifractal Dimension Representation

RF data-driven device fingerprinting through the use of deep learning has recently surfaced as a possible method for enabling secure device identification and authentication. Traditional approaches are commonly susceptible to the domain adaptation problem where a model trained on data collected under one domain performs badly when tested on data collected under a different domain. Some examples of a domain change include varying the location or environment of the device and varying the time or day of the data collection. In this work, we propose using multifractal analysis and the variance fractal dimension trajectory (VFDT) as a data representation input to the deep neural network to extract device fingerprints that are domain generalizable. We analyze the effectiveness of the proposed VFDT representation in detecting device-specific signatures from hardware-impaired IQ (in-phase and quadrature) signals, and we evaluate its robustness in real-world settings, using an experimental testbed of 30 WiFi-enabled Pycom devices. Our experimental results show that the proposed VFDT representation improves the scalability, robustness and generalizability of the deep learning models significantly compared to when using IQ data samples.

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arXiv2024

Domain-Agnostic Hardware Fingerprinting-Based Device Identifier for Zero-Trust IoT Security

Next-generation networks aim for comprehensive connectivity, interconnecting humans, machines, devices, and systems seamlessly. This interconnectivity raises concerns about privacy and security, given the potential network-wide impact of a single compromise. To address this challenge, the Zero Trust (ZT) paradigm emerges as a key method for safeguarding network integrity and data confidentiality. This work introduces EPS-CNN, a novel deep-learning-based wireless device identification framework designed to serve as the device authentication layer within the ZT architecture, with a focus on resource-constrained IoT devices. At the core of EPS-CNN, a Convolutional Neural Network (CNN) is utilized to generate the device identity from a unique RF signal representation, known as the Double-Sided Envelope Power Spectrum (EPS), which effectively captures the device-specific hardware characteristics while ignoring device-unrelated information. Experimental evaluations show that the proposed framework achieves over 99%, 93%, and 95% testing accuracy when tested in same-domain (day, location, and channel), cross-day, and cross-location scenarios, respectively. Our findings demonstrate the superiority of the proposed framework in enhancing the accuracy, robustness, and adaptability of deep learning-based methods, thus offering a pioneering solution for enabling ZT IoT device identification.

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arXiv2023

Domain-Adaptive Device Fingerprints for Network Access Authentication Through Multifractal Dimension Representation

RF data-driven device fingerprinting through the use of deep learning has recently surfaced as a potential solution for automated network access authentication. Traditional approaches are commonly susceptible to the domain adaptation problem where a model trained on data from one domain performs badly when tested on data from a different domain. Some examples of a domain change include varying the device location or environment and varying the time or day of data collection. In this work, we propose using multifractal analysis and the variance fractal dimension trajectory (VFDT) as a data representation input to the deep neural network to extract device fingerprints that are domain generalizable. We analyze the effectiveness of the proposed VFDT representation in detecting device-specific signatures from hardware-impaired IQ signals, and evaluate its robustness in real-world settings, using an experimental testbed of 30 WiFi-enabled Pycom devices under different locations and at different scales. Our results show that the VFDT representation improves the scalability, robustness and generalizability of the deep learning models significantly compared to when using raw IQ data.

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arXiv2023

EPS: Distinguishable IQ Data Representation for Domain-Adaptation Learning of Device Fingerprints

Deep learning (DL)-based RF fingerprinting (RFFP) technology has emerged as a powerful physical-layer security mechanism, enabling device identification and authentication based on unique device-specific signatures that can be extracted from the received RF signals. However, DL-based RFFP methods face major challenges concerning their ability to adapt to domain (e.g., day/time, location, channel, etc.) changes and variability. This work proposes a novel IQ data representation and feature design, termed Double-Sided Envelope Power Spectrum or EPS, that is proven to overcome the domain adaptation problems significantly. By accurately capturing device hardware impairments while suppressing irrelevant domain information, EPS offers improved feature selection for DL models in RFFP. Experimental evaluations demonstrate its effectiveness, achieving over 99% testing accuracy in same-day/channel/location evaluations and 93% accuracy in cross-day evaluations, outperforming the traditional IQ representation. Additionally, EPS excels in cross-location evaluations, achieving a 95% accuracy. The proposed representation significantly enhances the robustness and generalizability of DL-based RFFP methods, thereby presenting a transformative solution to IQ data-based device fingerprinting.

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arXiv2023

On the Impact of the Hardware Warm-Up Time on Deep Learning-Based RF Fingerprinting

Deep learning-based RF fingerprinting offers great potential for improving the security robustness of various emerging wireless networks. Although much progress has been done in enhancing fingerprinting methods, the impact of device hardware stabilization and warm-up time on the achievable fingerprinting performances has not received adequate attention. As such, this paper focuses on addressing this gap by investigating and shedding light on what could go wrong if the hardware stabilization aspects are overlooked. Specifically, our experimental results show that when the deep learning models are trained with data samples captured after the hardware stabilizes but tested with data captured right after powering on the devices, the device classification accuracy drops below 37%. However, when both the training and testing data are captured after the stabilization period, the achievable average accuracy exceeds 99%, when the model is trained and tested on the same day, and achieves 88% and 96% when the model is trained on one day but tested on another day, for the wireless and wired scenarios, respectively. Additionally, in this work, we leverage simulation and testbed experimentation to explain the cause behind the I/Q signal behavior observed during the device hardware warm-up time that led to the RF fingerprinting performance degradation. Furthermore, we release a large WiFi dataset, containing both unstable (collected during the warm-up period) and stable (collected after the warm-up period) captures across multiple days. Our work contributes datasets, explanations, and guidelines to enhance the robustness of RF fingerprinting in securing emerging wireless networks.

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arXiv2023

Deep Learning-Enabled Zero-Touch Device Identification: Mitigating the Impact of Channel Variability Through MIMO Diversity

Deep learning-enabled device fingerprinting has proven efficient in enabling automated identification and authentication of transmitting devices. It does so by leveraging the transmitters' unique features that are inherent to hardware impairments caused during manufacturing to extract device-specific signatures that can be exploited to uniquely distinguish and separate between (identical) devices. Though shown to achieve promising performances, hardware fingerprinting approaches are known to suffer greatly when the training data and the testing data are generated under different channels conditions that often change when time and/or location changes. To the best of our knowledge, this work is the first to use MIMO diversity to mitigate the impact of channel variability and provide a channel-resilient device identification over flat fading channels. Specifically, we show that MIMO can increase the device classification accuracy by up to about $50\%$ when model training and testing are done over the same channel and by up to about $70\%$ when training and testing are done over different fading channels.

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arXiv2023

HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication

New capabilities in wireless network security have been enabled by deep learning, which leverages patterns in radio frequency (RF) data to identify and authenticate devices. Open-set detection is an area of deep learning that identifies samples captured from new devices during deployment that were not part of the training set. Past work in open-set detection has mostly been applied to independent and identically distributed data such as images. In contrast, RF signal data present a unique set of challenges as the data forms a time series with non-linear time dependencies among the samples. We introduce a novel open-set detection approach based on the patterns of the hidden state values within a Convolutional Neural Network (CNN) Long Short-Term Memory (LSTM) model. Our approach greatly improves the Area Under the Precision-Recall Curve on LoRa, Wireless-WiFi, and Wired-WiFi datasets, and hence, can be used successfully to monitor and control unauthorized network access of wireless devices.

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arXiv2023

A Survey on AI/ML-Driven Intrusion and Misbehavior Detection in Networked Autonomous Systems: Techniques, Challenges and Opportunities

AI/ML-based intrusion detection systems (IDSs) and misbehavior detection systems (MDSs) have shown great potential in identifying anomalies in the network traffic of networked autonomous systems. Despite the vast research efforts, practical deployments of such systems in the real world have been limited. Although the safety-critical nature of autonomous systems and the vulnerability of learning-based techniques to adversarial attacks are among the potential reasons, the lack of objective evaluation and feasibility assessment metrics is one key reason behind the limited adoption of these systems in practical settings. This survey aims to address the aforementioned limitation by presenting an in-depth analysis of AI/ML-based IDSs/MDSs and establishing baseline metrics relevant to networked autonomous systems. Furthermore, this work thoroughly surveys recent studies in this domain, highlighting the evaluation metrics and gaps in the current literature. It also presents key findings derived from our analysis of the surveyed papers and proposes guidelines for providing AI/ML-based IDS/MDS solution approaches suitable for vehicular network applications. Our work provides researchers and practitioners with the needed tools to evaluate the feasibility of AI/ML-based IDS/MDS techniques in real-world settings, with the aim of facilitating the practical adoption of such techniques in emerging autonomous vehicular systems.

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arXiv2023

ADL-ID: Adversarial Disentanglement Learning for Wireless Device Fingerprinting Temporal Domain Adaptation

As the journey of 5G standardization is coming to an end, academia and industry have already begun to consider the sixth-generation (6G) wireless networks, with an aim to meet the service demands for the next decade. Deep learning-based RF fingerprinting (DL-RFFP) has recently been recognized as a potential solution for enabling key wireless network applications and services, such as spectrum policy enforcement and network access control. The state-of-the-art DL-RFFP frameworks suffer from a significant performance drop when tested with data drawn from a domain that is different from that used for training data. In this paper, we propose ADL-ID, an unsupervised domain adaption framework that is based on adversarial disentanglement representation to address the temporal domain adaptation for the RFFP task. Our framework has been evaluated on real LoRa and WiFi datasets and showed about 24% improvement in accuracy when compared to the baseline CNN network on short-term temporal adaptation. It also improves the classification accuracy by up to 9% on long-term temporal adaptation. Furthermore, we release a 5-day, 2.1TB, large-scale WiFi 802.11b dataset collected from 50 Pycom devices to support the research community efforts in developing and validating robust RFFP methods.

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arXiv2022

Uncovering the Portability Limitation of Deep Learning-Based Wireless Device Fingerprints

Recent device fingerprinting approaches rely on deep learning to extract device-specific features solely from raw RF signals to identify, classify and authenticate wireless devices. One widely known issue lies in the inability of these approaches to maintain good performances when the training data and testing data are collected under varying deployment domains. For example, when the learning model is trained on data collected from one receiver but tested on data collected from a different receiver, the performance degrades substantially compared to when both training and testing data are collected using the same receiver. The same also happens when considering other varying domains, like channel condition and protocol configuration. In this paper, we begin by explaining, through testbed experiments, the challenges these fingerprinting techniques face when it comes to domain portability. We will then present some ideas on how to go about addressing these challenges so as to make deep learning-based device fingerprinting more resilient to domain variability.

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arXiv2022

Tweak: Towards Portable Deep Learning Models for Domain-Agnostic LoRa Device Authentication

Deep learning based device fingerprinting has emerged as a key method of identifying and authenticating devices solely via their captured RF transmissions. Conventional approaches are not portable to different domains in that if a model is trained on data from one domain, it will not perform well on data from a different but related domain. Examples of such domains include the receiver hardware used for collecting the data, the day/time on which data was captured, and the protocol configuration of devices. This work proposes Tweak, a technique that, using metric learning and a calibration process, enables a model trained with data from one domain to perform well on data from another domain. This process is accomplished with only a small amount of training data from the target domain and without changing the weights of the model, which makes the technique computationally lightweight and thus suitable for resource-limited IoT networks. This work evaluates the effectiveness of Tweak vis-a-vis its ability to identify IoT devices using a testbed of real LoRa-enabled devices under various scenarios. The results of this evaluation show that Tweak is viable and especially useful for networks with limited computational resources and applications with time-sensitive missions.

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