arXiv subjects

Thomas C. Schmidt

Publications and source records attributed to Thomas C. Schmidt.

At least 37 records · Page 2Linked to original sources

arXiv2024

ReACKed QUICer: Measuring the Performance of Instant Acknowledgments in QUIC Handshakes

In this paper, we present a detailed performance analysis of QUIC instant ACK, a standard-compliant approach to reduce waiting times during the QUIC connection setup in common CDN deployments. To understand the root causes of the performance properties, we combine numerical analysis and the emulation of eight QUIC implementations using the QUIC Interop Runner. Our experiments comprehensively cover packet loss and non-loss scenarios, different round trip times, and TLS certificate sizes. To clarify instant ACK deployments in the wild, we conduct active measurements of 1M popular domain names. For almost all domain names under control of Cloudflare, Cloudflare uses instant ACK, which in fact improves performance. We also find, however, that instant ACK may lead to unnecessary retransmissions or longer waiting times under some network conditions, raising awareness of drawbacks of instant ACK in the future.

cs.NI↗

arXiv2024

Network Anomaly Detection in Cars: A Case for Time-Sensitive Stream Filtering and Policing

Connected vehicles are threatened by cyber-attacks as in-vehicle networks technologically approach (mobile) LANs with several wireless interconnects to the outside world. Malware that infiltrates a car today faces potential victims of constrained, barely shielded Electronic Control Units (ECUs). Many ECUs perform critical driving functions, which stresses the need for hardening security and resilience of in-vehicle networks in a multifaceted way. Future vehicles will comprise Ethernet backbones that differentiate services via Time-Sensitive Networking (TSN). The well-known vehicular control flows will follow predefined schedules and TSN traffic classifications. In this paper, we exploit this traffic classification to build a network anomaly detection system. We show how filters and policies of TSN can identify misbehaving traffic and thereby serve as distributed guards on the data link layer. On this lowest possible layer, our approach derives a highly efficient network protection directly from TSN. We classify link layer anomalies and micro-benchmark the detection accuracy in each class. Based on a topology derived from a real-world car and its traffic definitions we evaluate the detection system in realistic macro-benchmarks based on recorded attack traces. Our results show that the detection accuracy depends on how exact the specifications of in-vehicle communication are configured. Most notably for a fully specified communication matrix, our anomaly detection remains free of false-positive alarms, which is a significant benefit for implementing automated countermeasures in future vehicles.

cs.NI↗

arXiv2024

The Age of DDoScovery: An Empirical Comparison of Industry and Academic DDoS Assessments

Motivated by the impressive but diffuse scope of DDoS research and reporting, we undertake a multistakeholder (joint industry-academic) analysis to seek convergence across the best available macroscopic views of the relative trends in two dominant classes of attacks - direct-path attacks and reflection-amplification attacks. We first analyze 24 industry reports to extract trends and (in)consistencies across observations by commercial stakeholders in 2022. We then analyze ten data sets spanning industry and academic sources, across four years (2019-2023), to find and explain discrepancies based on data sources, vantage points, methods, and parameters. Our method includes a new approach: we share an aggregated list of DDoS targets with industry players who return the results of joining this list with their proprietary data sources to reveal gaps in visibility of the academic data sources. We use academic data sources to explore an industry-reported relative drop in spoofed reflection-amplification attacks in 2021-2022. Our study illustrates the value, but also the challenge, in independent validation of security-related properties of Internet infrastructure. Finally, we reflect on opportunities to facilitate greater common understanding of the DDoS landscape. We hope our results inform not only future academic and industry pursuits but also emerging policy efforts to reduce systemic Internet security vulnerabilities.

cs.CR↗

arXiv2024

From the Beginning: Key Transitions in the First 15 Years of DNSSEC

When the global rollout of the DNS Security Extensions (DNSSEC) began in 2005, a first-of-its-kind trial started: The complexity of a core Internet protocol was magnified in favor of better security for the overall Internet. Thereby, the scale of the loosely-federated delegation in DNS became an unprecedented cryptographic key management challenge. Though fundamental for current and future operational success, our community lacks a clear notion of how to empirically evaluate the process of securely transitioning keys. In this paper, we propose two building blocks to formally characterize and assess key transitions. First, the anatomy of key transitions, i.e., measurable and well-defined properties of key changes; and second, a novel classification model based on this anatomy for describing key transition practices in abstract terms. This abstraction allows for classifying operational behavior. We apply our proposed transition anatomy and transition classes to describe the global DNSSEC deployment. Specifically, we use measurements from the first 15 years of the DNSSEC rollout to detect and understand which key transitions have been used to what degree and which rates of errors and warnings occurred. In contrast to prior work, we consider all possible transitions and not only 1:1 key rollovers. Our results show measurable gaps between prescribed key management processes and key transitions in the wild. We also find evidence that such noncompliant transitions are needed in operations.

cs.CR↗

arXiv2024

A Review of Techniques for Ageing Detection and Monitoring on Embedded Systems

Embedded digital devices are progressively deployed in dependable or safety-critical systems. These devices undergo significant hardware ageing, particularly in harsh environments. This increases their likelihood of failure. It is crucial to understand ageing processes and to detect hardware degradation early for guaranteeing system dependability. In this survey, we review the core ageing mechanisms, identify and categorize general working principles of ageing detection and monitoring techniques for Commercial-Off-The-Shelf (COTS) components that are prevalent in embedded systems: Field Programmable Gate Arrays (FPGAs), microcontrollers, System-on-Chips (SoCs), and their power supplies. From our review, we find that online techniques are more widely applied on FPGAs than on other components, and see a rising trend towards machine learning application for analysing hardware ageing. Based on the reviewed literature, we identify research opportunities and potential directions of interest in the field. With this work, we intend to facilitate future research by systematically presenting all main approaches in a concise way.

cs.AR↗

arXiv2024

Studying the Degradation of Propagation Delay on FPGAs at the European XFEL

An increasing number of unhardened commercial-off-the-shelf embedded devices are deployed under harsh operating conditions and in highly-dependable systems. Due to the mechanisms of hardware degradation that affect these devices, ageing detection and monitoring are crucial to prevent critical failures. In this paper, we empirically study the propagation delay of 298 naturally-aged FPGA devices that are deployed in the European XFEL particle accelerator. Based on in-field measurements, we find that operational devices show significantly slower switching frequencies than unused chips, and that increased gamma and neutron radiation doses correlate with increased hardware degradation. Furthermore, we demonstrate the feasibility of developing machine learning models that estimate the switching frequencies of the devices based on historical and environmental data.

cs.AR↗

arXiv2024

Do CAA, CT, and DANE Interlink in Certificate Deployments? A Web PKI Measurement Study

Integrity and trust on the web build on X.509 certificates. Misuse or misissuance of these certificates threaten the Web PKI security model, which led to the development of several guarding techniques. In this paper, we study the DNS/DNSSEC records CAA and TLSA as well as CT logs from the perspective of the certificates in use. Our measurements comprise 4 million popular domains, for which we explore the existence and consistency of the different extensions. Our findings indicate that CAA is almost exclusively deployed in the absence of DNSSEC, while DNSSEC protected service names tend to not use the DNS for guarding certificates. Even though mainly deployed in a formally correct way, CAA CA-strings tend to not selectively separate CAs, and numerous domains hold certificates beyond the CAA semantic. TLSA records are repeatedly poorly maintained and occasionally occur without DNSSEC.

cs.CR↗

arXiv2024

Coordinating Cooperative Perception in Urban Air Mobility for Enhanced Environmental Awareness

The trend for Urban Air Mobility (UAM) is growing with prospective air taxis, parcel deliverers, and medical and industrial services. Safe and efficient UAM operation relies on timely communication and reliable data exchange. In this paper, we explore Cooperative Perception (CP) for Unmanned Aircraft Systems (UAS), considering the unique communication needs involving high dynamics and a large number of UAS. We propose a hybrid approach combining local broadcast with a central CP service, inspired by centrally managed U-space and broadcast mechanisms from automotive and aviation domains. In a simulation study, we show that our approach significantly enhances the environmental awareness for UAS compared to fully distributed approaches, with an increased communication channel load, which we also evaluate. These findings prompt a discussion on communication strategies for CP in UAM and the potential of a centralized CP service in future research.

cs.NI↗

arXiv2024

A Framework for the Systematic Assessment of Anomaly Detectors in Time-Sensitive Automotive Networks

Connected cars are susceptible to cyberattacks. Security and safety of future vehicles highly depend on a holistic protection of automotive components, of which the time-sensitive backbone network takes a significant role. These onboard Time-Sensitive Networks (TSNs) require monitoring for safety and -- as versatile platforms to host Network Anomaly Detection Systems (NADSs) -- for security. Still a thorough evaluation of anomaly detection methods in the context of hard real-time operations, automotive protocol stacks, and domain specific attack vectors is missing along with appropriate input datasets. In this paper, we present an assessment framework that allows for reproducible, comparable, and rapid evaluation of detection algorithms. It is based on a simulation toolchain, which contributes configurable topologies, traffic streams, anomalies, attacks, and detectors. We demonstrate the assessment of NADSs in a comprehensive in-vehicular network with its communication flows, on which we model traffic anomalies. We evaluate exemplary detection mechanisms and reveal how the detection performance is influenced by different combinations of TSN traffic flows and anomaly types. Our approach translates to other real-time Ethernet domains, such as industrial facilities, airplanes, and UAVs.

cs.NI↗

arXiv2024

Understanding IoT Domain Names: Analysis and Classification Using Machine Learning

In this paper, we investigate the domain names of servers on the Internet that are accessed by IoT devices performing machine-to-machine communications. Using machine learning, we classify between them and domain names of servers contacted by other types of devices. By surveying past studies that used testbeds with real-world devices and using lists of top visited websites, we construct lists of domain names of both types of servers. We study the statistical properties of the domain name lists and train six machine learning models to perform the classification. The word embedding technique we use to get the real-value representation of the domain names is Word2vec. Among the models we train, Random Forest achieves the highest performance in classifying the domain names, yielding the highest accuracy, precision, recall, and F1 score. Our work offers novel insights to IoT, potentially informing protocol design and aiding in network security and performance monitoring.

cs.NI↗

arXiv2024

How to Measure TLS, X.509 Certificates, and Web PKI: A Tutorial and Brief Survey

Transport Layer Security (TLS) is the base for many Internet applications and services to achieve end-to-end security. In this paper, we provide guidance on how to measure TLS deployments, including X.509 certificates and Web PKI. We introduce common data sources and tools, and systematically describe necessary steps to conduct sound measurements and data analysis. By surveying prior TLS measurement studies we find that diverging results are rather rooted in different setups instead of different deployments. To improve the situation, we identify common pitfalls and introduce a framework to describe TLS and Web PKI measurements. Where necessary, our insights are bolstered by a data-driven approach, in which we complement arguments by additional measurements.

cs.NI↗

arXiv2023

A Multilayered Security Infrastructure for Connected Vehicles -- First Lessons from the Field

Connected vehicles are vulnerable to manipulation and a broad attack surface can be used to intrude in-vehicle networks from anywhere on earth. In this work, we present an integrated security infrastructure comprising network protection, monitoring, incident management, and counteractions, which we built into a prototype based on a production car. Our vehicle implements a Software-Defined Networking Ethernet backbone to restrict communication routes, network anomaly detection to make misbehavior evident, virtual controller functions to enable agile countermeasures, and an automotive cloud defense center to analyse and manage incidents on vehicle fleets. We present first measurements and lessons learned from operating the prototype: many network attacks can be prevented through software-defined access control in the backbone; anomaly detection can reliably detect misbehavior but needs to improve on false positive rate; controller virtualization needs tailored frameworks to meet in-car requirements; and cloud defence enables fleet management and advanced countermeasures. Our findings indicate attack mitigation times in the vehicle from 257 ms to 328 ms and from 2,168 ms to 2,713 ms traversing the cloud.

cs.CR↗

arXiv2023

PUF for the Commons: Enhancing Embedded Security on the OS Level

Security is essential for the Internet of Things (IoT). Cryptographic operations for authentication and encryption commonly rely on random input of high entropy and secure, tamper-resistant identities, which are difficult to obtain on constrained embedded devices. In this paper, we design and analyze a generic integration of physically unclonable functions (PUFs) into the IoT operating system RIOT that supports about 250 platforms. Our approach leverages uninitialized SRAM to act as the digital fingerprint for heterogeneous devices. We ground our design on an extensive study of PUF performance in the wild, which involves SRAM measurements on more than 700 IoT nodes that aged naturally in the real-world. We quantify static SRAM bias, as well as the aging effects of devices and incorporate the results in our system. This work closes a previously identified gap of missing statistically significant sample sizes for testing the unpredictability of PUFs. Our experiments on COTS devices of 64 kB SRAM indicate that secure random seeds derived from the SRAM PUF provide 256 Bits-, and device unique keys provide more than 128 Bits of security. In a practical security assessment we show that SRAM PUFs resist moderate attack scenarios, which greatly improves the security of low-end IoT devices.

cs.CR↗

arXiv2023

Securing name resolution in the IoT: DNS over CoAP

In this paper, we present the design, implementation, and analysis of DNS over CoAP (DoC), a new proposal for secure and privacy-friendly name resolution of constrained IoT devices. We implement different design choices of DoC in RIOT, an open-source operating system for the IoT, evaluate performance measures in a testbed, compare with DNS over UDP and DNS over DTLS, and validate our protocol design based on empirical DNS IoT data. Our findings indicate that plain DoC is on par with common DNS solutions for the constrained IoT but significantly outperforms when additional standard features of CoAP are used such as caching. With OSCORE, we can save more than 10 kBytes of code memory compared to DTLS, when a CoAP application is already present, and retain the end-to-end trust chain with intermediate proxies, while leveraging features such as group communication or encrypted en-route caching. We also discuss a compression scheme for very restricted links that reduces data by up to 70%.

cs.NI↗

arXiv2023

Ageing Analysis of Embedded SRAM on a Large-Scale Testbed Using Machine Learning

Ageing detection and failure prediction are essential in many Internet of Things (IoT) deployments, which operate huge quantities of embedded devices unattended in the field for years. In this paper, we present a large-scale empirical analysis of natural SRAM wear-out using 154 boards from a general-purpose testbed. Starting from SRAM initialization bias, which each node can easily collect at startup, we apply various metrics for feature extraction and experiment with common machine learning methods to predict the age of operation for this node. Our findings indicate that even though ageing impacts are subtle, our indicators can well estimate usage times with an $R^2$ score of 0.77 and a mean error of 24% using regressors, and with an F1 score above 0.6 for classifiers applying a six-months resolution.

cs.AR↗

arXiv2023

SoK: A Data-driven View on Methods to Detect Reflective Amplification DDoS Attacks Using Honeypots

In this paper, we revisit the use of honeypots for detecting reflective amplification attacks. These measurement tools require careful design of both data collection and data analysis including cautious threshold inference. We survey common amplification honeypot platforms as well as the underlying methods to infer attack detection thresholds and to extract knowledge from the data. By systematically exploring the threshold space, we find most honeypot platforms produce comparable results despite their different configurations. Moreover, by applying data from a large-scale honeypot deployment, network telescopes, and a real-world baseline obtained from a leading DDoS mitigation provider, we question the fundamental assumption of honeypot research that convergence of observations can imply their completeness. Conclusively we derive guidance on precise, reproducible honeypot research, and present open challenges.

cs.CR↗

arXiv2023

Authenticated and Secure Automotive Service Discovery with DNSSEC and DANE

Automotive softwarization is progressing and future cars are expected to operate a Service-Oriented Architecture on multipurpose compute units, which are interconnected via a high-speed Ethernet backbone. The AUTOSAR architecture foresees a universal middleware called SOME/IP that provides the service primitives, interfaces, and application protocols on top of Ethernet and IP. SOME/IP lacks a robust security architecture, even though security is an essential in future Internet-connected vehicles. In this paper, we augment the SOME/IP service discovery with an authentication and certificate management scheme based on DNSSEC and DANE. We argue that the deployment of well-proven, widely tested standard protocols should serve as an appropriate basis for a robust and reliable security infrastructure in cars. Our solution enables on-demand service authentication in offline scenarios, easy online updates, and remains free of attestation collisions. We evaluate our extension of the common vsomeip stack and find performance values that fully comply with car operations.

cs.CR↗

arXiv2023

Dynamic Service-Orientation for Software-Defined In-Vehicle Networks

Modern In-Vehicle Networks (IVNs) are composed of a large number of devices and services linked via an Ethernet-based time-sensitive network. Communication in future IVNs will become more dynamic as services can be updated, added, or removed during runtime. This requires a flexible and adaptable IVN, for which Software-Defined Networking (SDN) is a promising candidate. In this paper, we show how SDN can be used to support a dynamic, service-oriented network architecture. We demonstrate our concept using the SOME/IP protocol, which is the most widely deployed implementation of automotive service-oriented architectures. In a simulation study, we evaluate the performance of SOME/IP-adaptive SDN control compared to standard Ethernet switching and non-optimized SDN. Our results show an expected overhead introduced by the central SDN controller, which is, however, reduced by up to 50% compared to SOME/IP-unaware SDN.For a large number of services, the setup time is in the order of milliseconds, which matches standard Ethernet switching. A SOME/IP-aware SDN controller can optimize the service discovery to improve adaptability, robustness, security, and Quality-of-Service of the IVN while remaining transparent to existing SOME/IP implementations.

cs.NI↗