arXiv subjects

Sebastian Steinhorst

Publications and source records attributed to Sebastian Steinhorst.

At least 19 recordsLinked to original sources

arXiv2026

Making Time-Sensitive Networking Deployable: A Comprehensive Lifecycle Architecture

Time-Sensitive Networking (TSN) provides deterministic guarantees and bounded latency for safety-critical applications across different domains. While the IEEE 802.1 TSN standards provide a comprehensive set of mechanisms for time synchronization, shaping, scheduling, resource reservation, and reliability, deploying TSN in real-world applications remains a complex, multi-step engineering task. Existing research primarily focuses on optimizing individual objectives, such as increasing schedulability, reducing latency, accelerating solution generation, or improving performance analysis. However, whether these extensively researched solutions can be deployed in real-world systems remains unanswered, as the assumptions made in these methodologies often differ from practical hardware constraints. Therefore, a broader, holistic view of end-to-end TSN deployment and management remains an open research question. Currently, there is a lack of a unified perspective, consensus, and coordinated research efforts focusing on a comprehensive end-to-end TSN deployment workflow. We present a comprehensive overview of the TSN deployment lifecycle, current challenges, limitations of existing tools, and future research directions for TSN deployment and management. Lastly, we identify key research gaps from a deployment perspective and provide guidance for the development of next-generation deployable TSN networks.

cs.NI↗

arXiv2026

The Spectrum Strikes Back: Infrared POV Attacks on Traffic Sign Classification

Traffic sign classification is a crucial task for autonomous vehicles, and numerous attacks against it have been identified. A majority of physical adversarial attacks involve attaching patches to traffic signs or projecting perturbations on them. While they demonstrate high effectiveness, they are perceptible to humans. At the same time, light-based attacks outside the human visible spectrum are known but have limitations in their dynamic adaptability. We propose a persistence-of-vision-based attack that operates in the near-infrared light spectrum. With the possibility of showing dynamic, remotely triggered content, this allows a stealthy physical adversarial attack against traffic sign classification. By identifying the optimal position through digital simulation, we conduct extensive real-world evaluations using two different traffic signs, 12 machine learning models from different families, multiple distances up to 20 meters, and varying illumination conditions. Our evaluation shows high attack success rates across our test scenarios. We propose near-infrared cutoff filters and a software-based detection mechanism as defenses, and tackle limitations of the near-infrared persistence of vision display by prototyping a human-visible RGB version of it.

cs.CR↗

arXiv2026

TSNBench: Benchmarking LLM Proficiency in Time-Sensitive Networking

We present TSNBench, the first benchmark for evaluating large language model (LLM) proficiency in Time-Sensitive Networking (TSN), a suite of IEEE 802.1 standards for deterministic communication with bounded latency in safety-critical domains such as autonomous vehicles, aviation, defense, and industrial automation. While LLMs have been extensively evaluated on general knowledge tasks, their capabilities in safety-critical networking domains remain largely unexplored. TSNBench comprises 939 expert-validated multiple-choice questions (MCQs) covering diverse TSN mechanisms, along with 100 open-ended Worst-Case Delay (WCD) computation tasks for Credit-Based Shaper (CBS) and Cyclic Queuing and Forwarding (CQF) across varying network topologies and traffic conditions. MCQ answers are validated by domain experts, and open-ended ground truth WCD values are computed using a verified Network Calculus (NC) solver for CBS and closed-form mathematical upper bounds for CQF. We evaluate 16 LLMs and find that although models achieve 67 to 95% accuracy on MCQs, they fail substantially on open-ended WCD computation. For CBS, only GPT-5 achieves a Mean Absolute Percentage Error (MAPE) of 36.2%, meaning its predicted WCD deviates by 36.2% of the actual TSN flow delay on average, while most models exceed 80%. For CQF, the best model achieves 41.8% MAPE, with most models clustering between 80% and 100%. Such errors are large relative to TSN latency budgets and can lead to violations of real-time constraints and unsafe configurations. TSNBench demonstrates that MCQ benchmarks may overestimate LLM capabilities in safety-critical networking domains.

cs.NI↗

arXiv2025

Introducing Large Language Models into the Design Flow of Time-Sensitive Networking

The growing demand for real-time, safety-critical systems has significantly increased both the adoption and complexity of Time-Sensitive Networking (TSN). Configuring an optimized TSN network is highly challenging, requiring careful planning, design, analysis, verification, validation, and deployment. Large Language Models (LLMs) have recently demonstrated strong capabilities in solving complex tasks, positioning them as promising candidates for automating end-to-end TSN deployment and management, referred to as TSN orchestration. This paper outlines the steps involved in TSN orchestration and the associated challenges. To assess the capabilities of existing LLMs, we conduct an initial proof-of-concept case study focused on TSN tasks across multiple models. Building on these insights, we propose an LLM-assisted orchestration framework. Unlike prior research on LLMs in computer networks, which has concentrated on general configuration and management, TSN-specific orchestration has not yet been investigated. We present the building blocks for automating TSN using LLMs, describe the proposed pipeline, and analyze opportunities and limitations for real-world deployment. This work provides the first roadmap toward assessing the feasibility of LLM-assisted TSN orchestration.

cs.NI↗

arXiv2025

Towards an Optimized Multi-Cyclic Queuing and Forwarding in Time Sensitive Networking with Time Injection

Cyclic Queuing and Forwarding (CQF) is a Time-Sensitive Networking (TSN) shaping mechanism that provides bounded latency and deterministic Quality of Service (QoS). However, CQF's use of a single cycle restricts its ability to support TSN traffic with diverse timing requirements. Multi-Cyclic Queuing and Forwarding (Multi-CQF) is a new and emerging TSN shaping mechanism that uses multiple cycles on the same egress port, allowing it to accommodate TSN flows with varied timing requirements more effectively than CQF. Despite its potential, current Multi-CQF configuration studies are limited, leading to a lack of comprehensive research, poor understanding of the mechanism, and limited adoption of Multi-CQF in practical applications. Previous work has shown the impact of Time Injection (TI), defined as the start time of Time-Triggered (TT) flows at the source node, on CQF queue resource utilization. However, the impact of TI has not yet been explored in the context of Multi-CQF. This paper introduces a set of constraints and leverages Domain Specific Knowledge (DSK) to reduce the search space for Multi-CQF configuration. Building on this foundation, we develop an open-source Genetic Algorithm (GA) and a hybrid GA-Simulated Annealing (GASA) approach to efficiently configure Multi-CQF networks and introduce TI in Multi-CQF to enhance schedulability. Experimental results show that our proposed algorithms significantly increase the number of scheduled TT flows compared to the baseline Simulated Annealing (SA) model, improving scheduling by an average of 15%. Additionally, GASA achieves a 20% faster convergence rate and lower time complexity, outperforming the SA model in speed, and efficiency.

cs.NI↗

arXiv2025

Toward Inherently Robust VLMs Against Visual Perception Attacks

Autonomous vehicles rely on deep neural networks (DNNs) for traffic sign recognition, lane centering, and vehicle detection, yet these models are vulnerable to attacks that induce misclassification and threaten safety. Existing defenses (e.g., adversarial training) often fail to generalize and degrade clean accuracy. We introduce Vehicle Vision-Language Models (V2LMs), fine-tuned vision-language models specialized for autonomous vehicle perception, and show that they are inherently more robust to unseen attacks without adversarial training, maintaining substantially higher adversarial accuracy than conventional DNNs. We study two deployments: Solo (task-specific V2LMs) and Tandem (a single V2LM for all three tasks). Under attacks, DNNs drop 33-74%, whereas V2LMs decline by under 8% on average. Tandem achieves comparable robustness to Solo while being more memory-efficient. We also explore integrating V2LMs in parallel with existing perception stacks to enhance resilience. Our results suggest V2LMs are a promising path toward secure, robust AV perception.

cs.CV↗

arXiv2025

FuzzSense: Towards A Modular Fuzzing Framework for Autonomous Driving Software

Fuzz testing to find semantic control vulnerabilities is an essential activity to evaluate the robustness of autonomous driving (AD) software. Whilst there is a preponderance of disparate fuzzing tools that target different parts of the test environment, such as the scenario, sensors, and vehicle dynamics, there is a lack of fuzzing strategies that ensemble these fuzzers to enable concurrent fuzzing, utilizing diverse techniques and targets. This research proposes FuzzSense, a modular, black-box, mutation-based fuzzing framework that is architected to ensemble diverse AD fuzzing tools. To validate the utility of FuzzSense, a LiDAR sensor fuzzer was developed as a plug-in, and the fuzzer was implemented in the new AD simulation platform AWSIM and Autoware.Universe AD software platform. The results demonstrated that FuzzSense was able to find vulnerabilities in the new Autoware.Universe software. We contribute to FuzzSense open-source with the aim of initiating a conversation in the community on the design of AD-specific fuzzers and the establishment of a community fuzzing framework to better target the diverse technology base of autonomous vehicles.

cs.CR↗

arXiv2025

Learning-Based Traffic Classification for Mixed-Critical Flows in Time-Sensitive Networking

Time-Sensitive Networking (TSN) supports multiple traffic types with diverse timing requirements, such as hard real-time (HRT), soft real-time (SRT), and Best Effort (BE) within a single network. To provide varying Quality of Service (QoS) for these traffic types, TSN incorporates different scheduling and shaping mechanisms. However, assigning traffic types to the proper scheduler or shaper, known as Traffic-Type Assignment (TTA), is a known NP-hard problem. Relying solely on domain expertise to make these design decisions can be inefficient, especially in complex network scenarios. In this paper, we present a proof-of-concept highlighting the advantages of a learning-based approach to the TTA problem. We formulate an optimization model for TTA in TSN and develop a Proximal Policy Optimization (PPO) based Deep Reinforcement Learning (DRL) model, called ``TTASelector'', to assign traffic types to TSN flows efficiently. Using synthetic and realistic test cases, our evaluation shows that TTASelector assigns a higher number of traffic types to HRT and SRT flows compared to the state-of-the-art Tabu Search-based metaheuristic method.

cs.NI↗

arXiv2024

A novel load distribution strategy for aggregators using IoT-enabled mobile devices

The rapid proliferation of Internet-of-things (IoT) as well as mobile devices such as Electric Vehicles (EVs), has led to unpredictable load at the grid. The demand to supply ratio is particularly exacerbated at a few grid aggregators (charging stations) with excessive demand due to the geographic location, peak time, etc. Existing solutions on demand response cannot achieve significant improvements based only on time-shifting the loads without considering the device properties such as charging modes and movement capabilities to enable geographic migration. Additionally, the information on the spare capacity at a few aggregators can aid in re-channeling the load from other aggregators facing excess demand to allow migration of devices. In this paper, we model these flexible properties of the devices as a mixed-integer non-linear problem (MINLP) to minimize excess load and the improve the utility (benefit) across all devices. We propose an online distributed low-complexity heuristic that prioritizes devices based on demand and deadlines to minimize the cumulative loss in utility. The proposed heuristic is tested on an exhaustive set of synthetic data and compared with solutions from a solver/optimization tool for the same runtime to show the impracticality of using a solver. A real-world EV testbed data is also tested with our proposed solution and other scheduling solutions to show the practicality of generating a feasible schedule and a loss improvement of at least 57.23%.

cs.MA↗

arXiv2024

Contextualizing Security and Privacy of Software-Defined Vehicles: A Literature Review and Industry Perspectives

The growing reliance on software in road vehicles has led to the emergence of Software-Defined Vehicles (SDV). This work analyzes SDV security and privacy through a systematic literature review complemented by an industry questionnaire across the automotive supply chain. The analysis is structured as four research questions and results in a security framework serving as a roadmap for SDV protection. The findings emphasize addressing mixed-criticality architectural challenges, deploying layered security mechanisms, and integrating privacy-preserving techniques. The results highlight the need to harmonize in-vehicle and cloud-based defenses to strengthen cybersecurity and V2X resilience in Intelligent Transportation Systems (ITS).

cs.CR↗

arXiv2024

Zero-Knowledge Location Privacy via Accurate Floating-Point SNARKs

We introduce Zero-Knowledge Location Privacy (ZKLP), enabling users to prove to third parties that they are within a specified geographical region while not disclosing their exact location. ZKLP supports varying levels of granularity, allowing for customization depending on the use case. To realize ZKLP, we introduce the first set of Zero-Knowledge Proof (ZKP) circuits that are fully compliant to the IEEE 754 standard for floating-point arithmetic. Our results demonstrate that our floating point circuits amortize efficiently, requiring only $64$ constraints per multiplication for $2^{15}$ single-precision floating-point multiplications. We utilize our floating point implementation to realize the ZKLP paradigm. In comparison to a baseline, we find that our optimized implementation has $15.9 \times$ less constraints utilizing single precision floating-point values, and $12.2 \times$ less constraints when utilizing double precision floating-point values. We demonstrate the practicability of ZKLP by building a protocol for privacy preserving peer-to-peer proximity testing - Alice can test if she is close to Bob by receiving a single message, without either party revealing any other information about their location. In such a configuration, Bob can create a proof of (non-)proximity in $0.26 s$, whereas Alice can verify her distance to about $470$ peers per second

cs.CR↗

arXiv2024

CyclicSim: Comprehensive Evaluation of Cyclic Shapers in Time-Sensitive Networking

Cyclic Queuing and Forwarding (CQF) is a key Time-Sensitive Networking (TSN) shaping mechanism that ensures bounded latency using a simple gate control list (GCL). Recently, variants of CQF, including Cycle Specific Queuing and Forwarding (CSQF) and Multi Cyclic Queuing and Forwarding (MCQF), have emerged. While popular TSN mechanisms such as the Time-Aware Shaper (TAS), Asynchronous Traffic Shaper (ATS), Credit-Based Shaper (CBS), and Strict Priority (SP) have been extensively studied, cyclic shapers have not been thoroughly evaluated. This paper presents a comprehensive analysis of CQF, CSQF, and MCQF, providing insights into their performance. We quantify delays through simulations and quantitative analysis on both synthetic and realistic networks. For the first time, we introduce an open-source OMNeT++ and INET4.4 based framework capable of modeling all three cyclic shaper variants. Our tool facilitates the validation of new algorithms and serves as a benchmark for cyclic shapers. Our evaluations reveal that MCQF supports diverse timing requirements, whereas CSQF, with its additional queue, often results in larger delays and jitter for some TT flows compared to CQF. Additionally, CSQF does not demonstrate significant advantages in TSN networks where propagation delays are less critical than in wide-area networks (WANs).

cs.NI↗

arXiv2024

Cluster-based Network Time Synchronization for Resilience with Energy Efficiency

Time synchronization of devices in Internet-of-Things (IoT) networks is one of the challenging problems and a pre-requisite for the design of low-latency applications. Although many existing solutions have tried to address this problem, almost all solutions assume all the devices (nodes) in the network are faultless. Furthermore, these solutions exchange a large number of messages to achieve synchronization, leading to significant communication and energy overhead. To address these shortcomings, we propose C-sync, a clustering-based decentralized time synchronization protocol that provides resilience against several types of faults with energy-efficient communication. C-sync achieves scalability by introducing multiple reference nodes in the network that restrict the maximum number of hops any node can have to its time source. The protocol is designed with a modular structure on the Contiki platform to allow application transitions. We evaluate C-sync on a real testbed that comprises over 40 Tmote Sky hardware nodes distributed across different levels in a building and show through experiments the fault resilience, energy efficiency, and scalability of the protocol. C-sync detects and isolates faults to a cluster and recovers quickly. The evaluation makes a qualitative comparison with state-of-the-art protocols and a quantitative comparison with a class of decentralized protocols (derived from GTSP) that provide synchronization with no/limited fault-tolerance. Results also show a reduction of 56.12% and 75.75% in power consumption in the worst-case and best-case scenarios, respectively, compared to GTSP, while achieving similar accuracy.

cs.DC↗

arXiv2024

SoK: Security of the Image Processing Pipeline for Camera-based Sensing in Autonomous Vehicles

Cameras capture images that are essential for many safety-critical tasks. To process these images, a complex pipeline with multiple layers is used. Security attacks on this pipeline can severely affect passenger safety and system performance. However, many attacks presented in scientific literature overlook the fact that there are different layers and, hence, the feasibility and impact of these attacks can vary. While there has been research to improve the quality and robustness of the image processing pipeline, these efforts are often orthogonal to security research without exploiting potential overlap and synergies. In this work, we aim to bridge this gap by combining security and robustness research for the image processing pipeline in autonomous vehicles. We thoroughly investigated the body of literature on the security and robustness of the image processing pipeline and selected 92 papers for deeper discussion in this SoK. For the security domain, we classify the risk of attacks using the automotive security standard ISO 21434, emphasizing the need to consider all layers for overall system security. With our online tool TARA-CAM, we propose an interactive method to perform threat analysis and risk assessment following the ISO standard. We also demonstrate how existing robustness research can help mitigate the impact of attacks, addressing the current research gap. Finally, we present PICT, an embedded open-source testbed that can influence various parameters across all layers, allowing researchers to analyze the effects of different defense strategies and attack impacts. With this SoK, we contribute a comprehensive discussion and systematic analysis of existing approaches to image processing pipeline security and robustness, together with an open-source tool and testbed that jointly facilitates hardening the image processing pipeline against existing and future security attacks.

cs.CR↗

arXiv2024

Quantifying the Impact of Frame Preemption on Combined TSN Shapers

Different scheduling mechanisms in Time Sensitive Networking (TSN) can be integrated together to design and support complex architectures with enhanced capabilities for mixed critical networks. Integrating Frame Preemption (FP) with Credit-Based Shaper (CBS) and Gate Control List (GCL) opens up different modes and configuration choices resulting in a complex evaluation of several possibilities and their impact on the Quality of Service (QoS). In this paper, we implement and quantify the integration of preemptive CBS with GCL by incorporating FP into the architecture. Our experiments show that the end-to-end delay of Audio Video Bridging (AVB) flows shaped by CBS reduces significantly (up to 40\%) when AVB flows are set to preemptable class. We further show that the jitter of Time Triggered (TT) traffic remains unaffected in "with Hold/Release" mode. Furthermore, we propose to introduce Guardband (GB) in the "without Hold/Release" to reduce the jitter of the TT flow. We compare all the different integration modes, starting with CBS with GCL, extending it further to FP. We evaluate all feasible combinations in both synthetic and realistic scenarios and offer recommendations for practical configuration methods.

cs.NI↗

arXiv2024

PTPsec: Securing the Precision Time Protocol Against Time Delay Attacks Using Cyclic Path Asymmetry Analysis

High-precision time synchronization is a vital prerequisite for many modern applications and technologies, including Smart Grids, Time-Sensitive Networking (TSN), and 5G networks. Although the Precision Time Protocol (PTP) can accomplish this requirement in trusted environments, it becomes unreliable in the presence of specific cyber attacks. Mainly, time delay attacks pose the highest threat to the protocol, enabling attackers to diverge targeted clocks undetected. With the increasing danger of cyber attacks, especially against critical infrastructure, there is a great demand for effective countermeasures to secure both time synchronization and the applications that depend on it. However, current solutions are not sufficiently capable of mitigating sophisticated delay attacks. For example, they lack proper integration into the PTP protocol, scalability, or sound evaluation with the required microsecond-level accuracy. This work proposes an approach to detect and counteract delay attacks against PTP based on cyclic path asymmetry measurements over redundant paths. For that, we provide a method to find redundant paths in arbitrary networks and show how this redundancy can be exploited to reveal and mitigate undesirable asymmetries on the synchronization path that cause the malicious clock divergence. Furthermore, we propose PTPsec, a secure PTP protocol and its implementation based on the latest IEEE 1588-2019 standard. With PTPsec, we advance the conventional PTP to support reliable delay attack detection and mitigation. We validate our approach on a hardware testbed, which includes an attacker capable of performing static and incremental delay attacks at a microsecond precision. Our experimental results show that all attack scenarios can be reliably detected and mitigated with minimal detection time.

cs.CR↗

arXiv2024

REACT: Autonomous Intrusion Response System for Intelligent Vehicles

Autonomous and connected vehicles are rapidly evolving, integrating numerous technologies and software. This progress, however, has made them appealing targets for cybersecurity attacks. As the risk of cyber threats escalates with this advancement, the focus is shifting from solely preventing these attacks to also mitigating their impact. Current solutions rely on vehicle security operation centers, where attack information is analyzed before deciding on a response strategy. However, this process can be time-consuming and faces scalability challenges, along with other issues stemming from vehicle connectivity. This paper proposes a dynamic intrusion response system integrated within the vehicle. This system enables the vehicle to respond to a variety of incidents almost instantly, thereby reducing the need for interaction with the vehicle security operation center. The system offers a comprehensive list of potential responses, a methodology for response evaluation, and various response selection methods. The proposed solution was implemented on an embedded platform. Two distinct cyberattack use cases served as the basis for evaluating the system. The evaluation highlights the system's adaptability, its ability to respond swiftly, its minimal memory footprint, and its capacity for dynamic system parameter adjustments. The proposed solution underscores the necessity and feasibility of incorporating dynamic response mechanisms in smart vehicles. This is a crucial factor in ensuring the safety and resilience of future smart mobility.

cs.CR↗

arXiv2023

Security Challenges in Autonomous Systems Design

Autonomous systems are emerging in many application domains. With the recent advancements in artificial intelligence and machine learning, sensor technology, perception algorithms and robotics, scenarios previously requiring strong human involvement can be handled by autonomous systems. With the independence from human control, cybersecurity of such systems becomes even more critical as no human intervention in case of undesired behavior is possible. In this context, this paper discusses emerging security challenges in autonomous systems design which arise in many domains such as autonomous incident response, risk assessment, data availability, systems interaction, trustworthiness, updatability, access control, as well as the reliability and explainability of machine learning methods. In all these areas, this paper thoroughly discusses the state of the art, identifies emerging security challenges and proposes research directions to address these challenges for developing secure autonomous systems.

cs.CR↗