M Sabbir Salek

Jean Michel Tine, Mohammed Aldeen, Abyad Enan et al.

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.

M Sabbir Salek, Mashrur Chowdhury, Muhaimin Bin Munir et al.

Existing threat modeling frameworks related to transportation cyber-physical systems (CPS) are often narrow in scope, labor-intensive, and require substantial cybersecurity expertise. To this end, we introduce the Transportation Cybersecurity and Resiliency Threat Modeling Framework (TraCR-TMF), a large language model (LLM)-based threat modeling framework for transportation CPS that requires limited cybersecurity expert intervention. TraCR-TMF identifies threats, potential attack techniques, and relevant countermeasures for transportation CPS. Three LLM-based approaches support these identifications: (i) a retrieval-augmented generation approach requiring no cybersecurity expert intervention, (ii) an in-context learning approach with low expert intervention, and (iii) a supervised fine-tuning approach with moderate expert intervention. TraCR-TMF offers LLM-based attack path identification for critical assets based on vulnerabilities across transportation CPS entities. Additionally, it incorporates the Common Vulnerability Scoring System (CVSS) scores of known exploited vulnerabilities to prioritize threat mitigations. The framework was evaluated through two cases. First, the framework identified relevant attack techniques for various transportation CPS applications, 73% of which were validated by cybersecurity experts as correct. Second, the framework was used to identify attack paths for a target asset in a real-world cyberattack incident. TraCR-TMF successfully predicted exploitations, like lateral movement of adversaries, data exfiltration, and data encryption for ransomware, as reported in the incident. These findings show the efficacy of TraCR-TMF in transportation CPS threat modeling, while reducing the need for extensive involvement of cybersecurity experts. To facilitate real-world adoptions, all our codes are shared via an open-source repository.

M Sabbir Salek, Abdullah Al Mamun, Mashrur Chowdhury

This study developed a generative adversarial network (GAN)-based defense method for traffic sign classification in an autonomous vehicle (AV), referred to as the attack-resilient GAN (AR-GAN). The novelty of the AR-GAN lies in (i) assuming zero knowledge of adversarial attack models and samples and (ii) providing consistently high traffic sign classification performance under various adversarial attack types. The AR-GAN classification system consists of a generator that denoises an image by reconstruction, and a classifier that classifies the reconstructed image. The authors have tested the AR-GAN under no-attack and under various adversarial attacks, such as Fast Gradient Sign Method (FGSM), DeepFool, Carlini and Wagner (C&W), and Projected Gradient Descent (PGD). The authors considered two forms of these attacks, i.e., (i) black-box attacks (assuming the attackers possess no prior knowledge of the classifier), and (ii) white-box attacks (assuming the attackers possess full knowledge of the classifier). The classification performance of the AR-GAN was compared with several benchmark adversarial defense methods. The results showed that both the AR-GAN and the benchmark defense methods are resilient against black-box attacks and could achieve similar classification performance to that of the unperturbed images. However, for all the white-box attacks considered in this study, the AR-GAN method outperformed the benchmark defense methods. In addition, the AR-GAN was able to maintain its high classification performance under varied white-box adversarial perturbation magnitudes, whereas the performance of the other defense methods dropped abruptly at increased perturbation magnitudes.

M Sabbir Salek, Shaozhi Li, Mashrur Chowdhury

The perception module in autonomous vehicles (AVs) relies heavily on deep learning-based models to detect and identify various objects in their surrounding environment. An AV traffic sign classification system is integral to this module, which helps AVs recognize roadway traffic signs. However, adversarial attacks, in which an attacker modifies or alters the image captured for traffic sign recognition, could lead an AV to misrecognize the traffic signs and cause hazardous consequences. Deepfake presents itself as a promising technology to be used for such adversarial attacks, in which a deepfake traffic sign would replace a real-world traffic sign image before the image is fed to the AV traffic sign classification system. In this study, the authors present how a generative adversarial network-based deepfake attack can be crafted to fool the AV traffic sign classification systems. The authors developed a deepfake traffic sign image detection strategy leveraging hybrid quantum-classical neural networks (NNs). This hybrid approach utilizes amplitude encoding to represent the features of an input traffic sign image using quantum states, which substantially reduces the memory requirement compared to its classical counterparts. The authors evaluated this hybrid deepfake detection approach along with several baseline classical convolutional NNs on real-world and deepfake traffic sign images. The results indicate that the hybrid quantum-classical NNs for deepfake detection could achieve similar or higher performance than the baseline classical convolutional NNs in most cases while requiring less than one-third of the memory required by the shallowest classical convolutional NN considered in this study.

Reek Majumder, Gurcan Comert, David Werth et al.

The network of services, including delivery, farming, and environmental monitoring, has experienced exponential expansion in the past decade with Unmanned Aerial Vehicles (UAVs). Yet, UAVs are not robust enough against cyberattacks, especially on the Controller Area Network (CAN) bus. The CAN bus is a general-purpose vehicle-bus standard to enable microcontrollers and in-vehicle computers to interact, primarily connecting different Electronic Control Units (ECUs). In this study, we focus on solving some of the most critical security weaknesses in UAVs by developing a novel graph-based intrusion detection system (IDS) leveraging the Uncomplicated Application-level Vehicular Communication and Networking (UAVCAN) protocol. First, we decode CAN messages based on UAVCAN protocol specification; second, we present a comprehensive method of transforming tabular UAVCAN messages into graph structures. Lastly, we apply various graph-based machine learning models for detecting cyber-attacks on the CAN bus, including graph convolutional neural networks (GCNNs), graph attention networks (GATs), Graph Sample and Aggregate Networks (GraphSAGE), and graph structure-based transformers. Our findings show that inductive models such as GATs, GraphSAGE, and graph-based transformers can achieve competitive and even better accuracy than transductive models like GCNNs in detecting various types of intrusions, with minimum information on protocol specification, thus providing a generic robust solution for CAN bus security for the UAVs. We also compared our results with baseline single-layer Long Short-Term Memory (LSTM) and found that all our graph-based models perform better without using any decoded features based on the UAVCAN protocol, highlighting higher detection performance with protocol-independent capability.

Reek Majumder, Jacquan Pollard, M Sabbir Salek et al.

The environmental impacts of global warming driven by methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4. Several data-driven machine learning (ML) models were tested to determine how well they identified fugitive CH4 and its related intensity in the affected areas. Various meteorological characteristics, including wind speed, temperature, pressure, relative humidity, water vapor, and heat flux, were included in the simulation. We used the ensemble learning method to determine the best-performing weighted ensemble ML models built upon several weaker lower-layer ML models to (i) detect the presence of CH4 as a classification problem and (ii) predict the intensity of CH4 as a regression problem.