arXiv subjects

Abyad Enan

Publications and source records attributed to Abyad Enan.

11 recordsLinked to original sources

arXiv2026

Structured Driving-State Narratives for Small Language Model-Based GNSS Spoofing Detection

Autonomous vehicles (AVs) depend on reliable Global Navigation Satellite System (GNSS) positioning. However, spoofed GNSS signals can induce plausible but incorrect vehicle states. This study develops a small language model (SLM)-based framework for detecting and classifying GNSS spoofing attacks by comparing vehicle behaviors independently derived from GNSS and other sensing sources. The framework converts independent driving states from GNSS and other sensing sources into structured semantic narratives that are provided to an SLM for spoofing detection and attack classification. The performance of the SLM-based framework is compared with large language models (LLMs) fine-tuned on identical training data and evaluated on the same test set. The evaluation considers five classes: no attack, overshoot attack, stopped attack, turn-by-turn attack, and wrong-turn attack. The framework is also evaluated with geographically unseen field data collected in Clemson, South Carolina, United States. Experimental results indicate that the evaluated SLMs achieve performance similar to the LLMs, achieving an average accuracy of 96.99%, precision of 99.05%, recall of 95.59%, and F1-score of 97.18%. In terms of computational efficiency and resource utilization, the SLMs demonstrate advantages over the LLMs by requiring lower inference latency and less GPU memory during both fine-tuning and inference. Evaluation using field data collected in a geographically distinct location further demonstrated its efficacy. The presented framework can detect and classify GNSS spoofing attacks in real-time while requiring relatively low computational and memory resources, and is therefore suitable for deployment on resource-constrained vehicular computing platforms.

cs.CR↗

arXiv2026

In-Vehicle Digital Twin-Based Collision Warning Framework with Sybil Attack Detection

Connected Vehicles (CVs) rely extensively on communication technologies to enable data-driven predictive analyses for enhancing performance and safety. These communication channels can be exploited by adversaries to launch cyberattacks such as Sybil attacks, which could threaten both safety-critical and mobility applications, leaving CVs vulnerable and putting human lives at risk. As CV deployment continues to expand, the need to detect and mitigate cyberattacks in real-time becomes increasingly urgent. This study presents an in-vehicle Digital Twin (DT)-based collision warning framework with built-in capabilities for Sybil attacks detection. The framework integrates a Temporal Convolutional Network (TCN) for learning temporal dependencies in vehicle trajectory data and Hierarchical Navigable Small World (HNSW) algorithms for efficient similarity-based classification. Our framework is evaluated on real-world Sybil attack data, collected through field experiments. The framework achieved accuracy, recall, and F1 scores of 0.984, 1.00, and 0.944, respectively, in detecting Sybil-generated fake vehicles. During the safety evaluation, the framework reduced the mean Time Exposed Time-To-Collision (TET) and mean Time Integrated Time-To-Collision (TIT) of near-collision events by 88% and 72%, respectively. Furthermore, real-world feasibility evaluation shows that the framework conformed to the standardized maximum allowable latency for safety applications and operated well within the capacity of modern processors -- demonstrating the promise of an in-vehicle DT-based framework as an attack mitigation mechanism against Sybil attacks for next-generation CVs.

cs.CR↗

arXiv2025

Quantum-Classical Hybrid Framework for Zero-Day Time-Push GNSS Spoofing Detection

Global Navigation Satellite Systems (GNSS) are critical for Positioning, Navigation, and Timing (PNT) applications. However, GNSS are highly vulnerable to spoofing attacks, where adversaries transmit counterfeit signals to mislead receivers. Such attacks can lead to severe consequences, including misdirected navigation, compromised data integrity, and operational disruptions. Most existing spoofing detection methods depend on supervised learning techniques and struggle to detect novel, evolved, and unseen attacks. To overcome this limitation, we develop a zero-day spoofing detection method using a Hybrid Quantum-Classical Autoencoder (HQC-AE), trained solely on authentic GNSS signals without exposure to spoofed data. By leveraging features extracted during the tracking stage, our method enables proactive detection before PNT solutions are computed. We focus on spoofing detection in static GNSS receivers, which are particularly susceptible to time-push spoofing attacks, where attackers manipulate timing information to induce incorrect time computations at the receiver. We evaluate our model against different unseen time-push spoofing attack scenarios: simplistic, intermediate, and sophisticated. Our analysis demonstrates that the HQC-AE consistently outperforms its classical counterpart, traditional supervised learning-based models, and existing unsupervised learning-based methods in detecting zero-day, unseen GNSS time-push spoofing attacks, achieving an average detection accuracy of 97.71% with an average false negative rate of 0.62% (when an attack occurs but is not detected). For sophisticated spoofing attacks, the HQC-AE attains an accuracy of 98.23% with a false negative rate of 1.85%. These findings highlight the effectiveness of our method in proactively detecting zero-day GNSS time-push spoofing attacks across various stationary GNSS receiver platforms.

cs.LG↗

arXiv2025

Real-Time Conflict Prediction for Large Truck Merging in Mixed Traffic at Work Zone Lane Closures

Large trucks substantially contribute to work zone-related crashes, primarily due to their large size and blind spots. When approaching a work zone, large trucks often need to merge into an adjacent lane because of lane closures caused by construction activities. This study aims to enhance the safety of large truck merging maneuvers in work zones by evaluating the risk associated with merging conflicts and establishing a decision-making strategy for merging based on this risk assessment. To predict the risk of large trucks merging into a mixed traffic stream within a work zone, a Long Short-Term Memory (LSTM) neural network is employed. For a large truck intending to merge, it is critical that the immediate downstream vehicle in the target lane maintains a minimum safe gap to facilitate a safe merging process. Once a conflict-free merging opportunity is predicted, large trucks are instructed to merge in response to the lane closure. Our LSTM-based conflict prediction method is compared against baseline approaches, which include probabilistic risk-based merging, 50th percentile gap-based merging, and 85th percentile gap-based merging strategies. The results demonstrate that our method yields a lower conflict risk, as indicated by reduced Time Exposed Time-to-Collision (TET) and Time Integrated Time-to-Collision (TIT) values relative to the baseline models. Furthermore, the findings indicate that large trucks that use our method can perform early merging while still in motion, as opposed to coming to a complete stop at the end of the current lane prior to closure, which is commonly observed with the baseline approaches.

cs.LG↗

arXiv2025

Understanding the Risks of Asphalt Art to the Reliability of Vision-Based Perception Systems

Artistic crosswalks featuring asphalt art, introduced by different organizations in recent years, aim to enhance the visibility and safety of pedestrians. However, their visual complexity may interfere with surveillance systems that rely on vision-based object detection models. In this study, we investigate the impact of asphalt art on pedestrian detection performance of a pretrained vision-based object detection model. We construct realistic crosswalk scenarios by compositing various street art patterns into a fixed surveillance scene and evaluate the model's performance in detecting pedestrians on asphalt-arted crosswalks under both benign and adversarial conditions. A benign case refers to pedestrian crosswalks painted with existing normal asphalt art, whereas an adversarial case involves digitally crafted or altered asphalt art perpetrated by an attacker. Our results show that while simple, color-based designs have minimal effect, complex artistic patterns, particularly those with high visual salience, can significantly degrade pedestrian detection performance. Furthermore, we demonstrate that adversarially crafted asphalt art can be exploited to deliberately obscure real pedestrians or generate non-existent pedestrian detections. These findings highlight a potential vulnerability in urban vision-based pedestrian surveillance systems, and underscore the importance of accounting for environmental visual variations when designing robust pedestrian perception models.

cs.CV↗

arXiv2025

Precision-Aware Video Compression for Reducing Bandwidth Requirements in Video Communication for Vehicle Detection-Based Applications

Computer vision has become a popular tool in intelligent transportation systems (ITS), enabling various applications through roadside traffic cameras that capture video and transmit it in real time to computing devices within the same network. The efficiency of this video transmission largely depends on the available bandwidth of the communication system. However, limited bandwidth can lead to communication bottlenecks, hindering the real-time performance of ITS applications. To mitigate this issue, lossy video compression techniques can be used to reduce bandwidth requirements, at the cost of degrading video quality. This degradation can negatively impact the accuracy of applications that rely on real-time vehicle detection. Additionally, vehicle detection accuracy is influenced by environmental factors such as weather and lighting conditions, suggesting that compression levels should be dynamically adjusted in response to these variations. In this work, we utilize a framework called Precision-Aware Video Compression (PAVC), where a roadside video camera captures footage of vehicles on roadways, compresses videos, and then transmits them to a processing unit, running a vehicle detection algorithm for safety-critical applications, such as real-time collision risk assessment. The system dynamically adjusts the video compression level based on current weather and lighting conditions to maintain vehicle detection accuracy while minimizing bandwidth usage. Our results demonstrate that PAVC improves vehicle detection accuracy by up to 13% and reduces communication bandwidth requirements by up to 8.23x in areas with moderate bandwidth availability. Moreover, in locations with severely limited bandwidth, PAVC reduces bandwidth requirements by up to 72x while preserving vehicle detection performance.

cs.CV↗

arXiv2025

Real-World Evaluation of Protocol-Compliant Denial-of-Service Attacks on C-V2X-based Forward Collision Warning Systems

Cellular Vehicle-to-Everything (C-V2X) technology enables low-latency, reliable communications essential for safety applications such as a Forward Collision Warning (FCW) system. C-V2X deployments operate under strict protocol compliance with the 3rd Generation Partnership Project (3GPP) and the Society of Automotive Engineers Standard (SAE) J2735 specifications to ensure interoperability. This paper presents a real-world testbed evaluation of protocol-compliant Denial-of-Service (DoS) attacks using User Datagram Protocol (UDP) flooding and oversized Basic Safety Message (BSM) attacks that 7 exploit transport- and application-layer vulnerabilities in C-V2X. The attacks presented in this study transmit valid messages over standard PC5 sidelinks, fully adhering to 3GPP and SAE J2735 specifications, but at abnormally high rates and with oversized payloads that overload the receiver resources without breaching any protocol rules such as IEEE 1609. Using a real-world connected vehicle 11 testbed with commercially available On-Board Units (OBUs), we demonstrate that high-rate UDP flooding and oversized payload of BSM flooding can severely degrade FCW performance. Results show that UDP flooding alone reduces packet delivery ratio by up to 87% and increases latency to over 400ms, while oversized BSM floods overload receiver processing resources, delaying or completely suppressing FCW alerts. When UDP and BSM attacks are executed simultaneously, they cause near-total communication failure, preventing FCW warnings entirely. These findings reveal that protocol-compliant communications do not necessarily guarantee safe or reliable operation of C-V2X-based safety applications.

cs.CR↗

arXiv2025

GAN-Based Single-Stage Defense for Traffic Sign Classification Under Adversarial Patch

Computer vision plays a critical role in ensuring the safe navigation of autonomous vehicles (AVs). An AV perception module facilitates safe navigation. This module enables AVs to recognize traffic signs, traffic lights, and various road users. However, the perception module is vulnerable to adversarial attacks, which can compromise its accuracy and reliability. One such attack is the adversarial patch attack (APA), an attack in which an adversary strategically places a specially crafted sticker on an object to deceive object classifiers. Such an APA can cause AVs to misclassify traffic signs, leading to catastrophic incidents. To enhance the security of an AV perception system against APAs, this study develops a Generative Adversarial Network (GAN)-based single-stage defense strategy for traffic sign classification. This approach is tailored to defend against APAs across different classes of traffic signs, without prior knowledge of a patch's design, and is effective against patches of varying sizes. In addition, our single-stage defense is computationally efficient, requiring significantly lower computation time than existing multi-stage defenses, making it suitable for real-time deployment in autonomous driving systems. Compared to a classifier without any defense mechanism, our experimental analysis demonstrates that the defense strategy presented in this paper improves our classifier's accuracy under APA conditions by up to 90% considering the traffic sign classes considered in this study. and overall classification accuracy is enhanced by 55% for all traffic signs considered in this study. Our defense strategy is model agnostic, making it applicable to any traffic sign classifier, regardless of the underlying classification model.

cs.CV↗

arXiv2024

Quantum Annealing-Enhanced Virtual Traffic Lights and its Evaluation Using a Quantum-in-the-Loop Simulation Testbed

Virtual Traffic Light (VTL) is a traffic control method that does not require traffic signal-related infrastructure for roadway intersections. Connected vehicles (CVs) are given right-of-way based on prevailing traffic conditions, such as estimated times of arrival (ETAs) of vehicles, the number of CVs in different approaches, and their emissions. These factors are considered in line with the objectives of the VTL application. Aiming to optimize traffic flow by reducing delays, VTL generates Signal Phase and Timing (SPaT) data for CVs approaching an intersection. Our VTL method considers the delay each CV would cause for other CVs if given the right-of-way. However, the stochastic nature of vehicle arrivals at intersections increases the complexity of the optimization problem, making it challenging for classical computers to determine optimal solutions in real-time. To address this limitation, we develop a VTL method designed to minimize stopped delays for CVs at an intersection by leveraging the efficacies of existing quantum computers that determine the best outcome from all possible combinations. This method employs Quadratic Unconstrained Binary Optimization (QUBO), a mathematical framework commonly used in quantum computing, to formulate the VTL problem as a stopped-delay-minimization challenge. To evaluate our method for roadway traffic with varying traffic volumes, we integrate an open-source microscopic roadway traffic simulator, Simulation for Urban Mobility (SUMO), with a cloud-based D-Wave quantum computer. Our analysis reveals that our quantum computing-supported VTL outperforms the classical optimization-based VTL by significantly reducing stopped delays at intersections and travel time through the roadway sections crossing the intersections.

cs.OH↗

arXiv2024

Crash Severity Risk Modeling Strategies under Data Imbalance

This study investigates crash severity risk modeling strategies for work zones involving large vehicles (i.e., trucks, buses, and vans) under crash data imbalance between low-severity (LS) and high-severity (HS) crashes. We utilized crash data involving large vehicles in South Carolina work zones from 2014 to 2018, which included four times more LS crashes than HS crashes. The objective of this study is to evaluate the crash severity prediction performance of various statistical, machine learning, and deep learning models under different feature selection and data balancing techniques. Findings highlight a disparity in LS and HS predictions, with lower accuracy for HS crashes due to class imbalance and feature overlap. Discriminative Mutual Information (DMI) yields the most effective feature set for predicting HS crashes without requiring data balancing, particularly when paired with gradient boosting models and deep neural networks such as CatBoost, NeuralNetTorch, XGBoost, and LightGBM. Data balancing techniques such as NearMiss-1 maximize HS recall when combined with DMI-selected features and certain models such as LightGBM, making them well-suited for HS crash prediction. Conversely, RandomUnderSampler, HS Class Weighting, and RandomOverSampler achieve more balanced performance, which is defined as an equitable trade-off between LS and HS metrics, especially when applied to NeuralNetTorch, NeuralNetFastAI, CatBoost, LightGBM, and Bayesian Mixed Logit (BML) using merged feature sets or models without feature selection. The insights from this study offer safety analysts guidance on selecting models, feature selection, and data balancing techniques aligned with specific safety goals, providing a robust foundation for enhancing work-zone crash severity prediction.

cs.LG↗

arXiv2024

Robust hardware Trojan detection leveraging dual-domain features and stacked ensemble learning

Cyber-physical systems rely on integrated circuits (ICs), making them vulnerable to hardware Trojans that can remain dormant until triggered, causing functional disruption or information leakage. Detecting these stealthy attacks is challenging because they introduce only subtle changes in circuit behavior. We present a golden-chip-free hardware Trojan detection framework that combines time-domain and frequency-domain features extracted from side-channel power traces. The framework evaluates six artificial intelligence models, including random forest, gradient boosting, naive Bayes, deep neural network, long short-term memory, and graph neural network, and integrates them using a stacked ensemble classifier. Evaluation on the AES-Trojan benchmark demonstrates that the proposed ensemble consistently outperforms the individual baseline models, achieving a macro-averaged ROC-AUC of 0.987. The results show that combining dual-domain feature extraction with stacked ensemble learning enables accurate and robust detection of hardware Trojans directly from side-channel emissions without requiring a trusted reference IC.

cs.CR↗