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Martin Strohmeier

Publications and source records attributed to Martin Strohmeier.

At least 19 recordsLinked to original sources

Chi-MERA: Defending Orbit-Based Authentication of LEO Satellites with the Space Oddity of MLAT (Long Version)

An active research area, physical layer security can be a last resort in insecure legacy systems, a resource-efficient alternative, or a complementary backup for cryptographic techniques. In this paper, we demonstrate that previously established authentication schemes for LEO satellites are vulnerable to attackers that control multiple devices. In extensive experiments, such attackers produce false positive rates (FPR) of up to 40%. Based on a root cause analysis, we develop a novel scheme, called Chi-MERA, that improves authentication performance and is robust against multi-device attackers. Our scheme uses two enhancements: (1) multilateration (MLAT) to clearly distinguish between attackers and legitimate satellites, and (2), a new resilient signature scheme without dependency on a single dedicated reference receiver. Our evaluation shows that exploiting MLAT characteristics such as residual analysis to classify vastly different signal source categories (orbit vs. non-orbit) is highly effective. In extensive simulations, we show that the new authentication achieves low FPR of $<$2% and false negative rates of $<$3% for accidentally rejecting valid signals. Furthermore, our scheme's defense scales linearly: $n$ receivers reliably withstand spoofing (FPR $< 1%$) from attackers with $\frac{n}{2}$ devices.

cs.CR

Systematic Security Analysis of the Iridium Satellite Radio Link

The Iridium Low Earth Orbit (LEO) satellite constellation remains a unique provider of global communications for critical industries, governments, and private users, serving over 2.5 million active subscribers despite recent market competition. In contrast to terrestrial wireless standards such as 3GPP, Iridium protocol specifications are proprietary and have not undergone rigorous, public, and systematic security evaluation. In this work, we present the first comprehensive security analysis of Iridium authentication and radio link protocols. We reverse engineer Iridium SIM-based authentication mechanism and demonstrate that the secret key can be extracted from the SIM card, enabling full device cloning and impersonation attacks. Leveraging a month-long dataset of Iridium up- and downlink satellite traffic, we further show that nearly all signaling and radio communication protocols currently in use lack encryption, resulting in the exposure of sensitive information in cleartext over the air such as login credentials and large volumes of personal data. Finally, we develop custom software-defined radio (SDR) tools to carry out spoofing and jamming attacks, revealing that modestly equipped adversaries can inject falsified messages or disrupt the Iridium service locally due to the absence of source authentication. Our findings uncover systemic vulnerabilities in the Iridium radio link and highlight the urgent need for users of critical applications to transition to more secure communication radio links.

cs.CR

DCeption: Real-world Wireless Man-in-the-Middle Attacks Against CCS EV Charging

The adoption of Electric Vehicles (EVs) is happening at a rapid pace. To ensure fast and safe charging, complex communication is required between the vehicle and the charging station. In the globally used Combined Charging System (CCS), this communication is carried over the HomePlug Green PHY (HPGP) physical layer. However, HPGP is known to suffer from wireless leakage, which may expose this data link to nearby attackers. In this paper, we examine active wireless attacks against CCS, and study the impact they can have. We present the first real-time Software-Defined Radio (SDR) implementation of HPGP, granting unprecedented access to the communications within the charging cables. We analyze the characteristics of 2,750 real-world charging sessions to understand the timing constraints for hijacking. Using novel techniques to increase the attacks' reliability, we design a robust wireless Man-in-the-Middle evaluation framework for CCS. We demonstrate full control over TLS usage and CCS protocol version negotiation, including TLS stripping attacks. We investigate how real devices respond to safety-critical MitM attacks, which modify power delivery information, and found target vehicles to be highly permissive. First, we caused a vehicle to display charging power exceeding 900 kW on the dashboard, while receiving only 40 kW. Second, we remotely overcharged a vehicle, at twice the requested current for 17 seconds before the vehicle triggered the emergency shutdown. Finally, we propose a backwards-compatible, downgrade-proof protocol extension to mitigate the underlying vulnerabilities.

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Secure and Scalable Rerouting in LEO Satellite Networks

Resilient routing in large-scale Low Earth Orbit (LEO) satellite networks remains a key challenge due to frequent and unpredictable link and node failures, potentially in response to cybersecurity breaches. While prior work has explored rerouting strategies with various levels of network awareness, their relative tradeoffs under dynamic failure conditions remain underexplored. In this work, we extend the Deep Space Network Simulator (DSNS) to systematically compare three rerouting paradigms, each differing in the scope of failure knowledge available to each node. We compare local neighbor-based, segment-based and global-knowledge-based rerouting as well as a naive source routing solution that is unaware of failures. Our main goal is to evaluate how the breadth of failure awareness impacts routing performance and resilience under failures, both random and targeted. We measure delivery ratio, latency, rerouting overhead, and loop occurrence. Our findings show the potential of segment-based rerouting to achieve a favorable tradeoff between local responsiveness and global coordination, offering resilience benefits with minimal overhead--insights that can inform future fault-tolerant satellite network design.

cs.NI

DSNS: The Deep Space Network Simulator

Simulation tools are commonly used in the development and testing of new protocols or new networks. However, as satellite networks start to grow to encompass thousands of nodes, and as companies and space agencies begin to realize the interplanetary internet, existing satellite and network simulation tools have become impractical for use in this context. We therefore present the Deep Space Network Simulator (DSNS): a new network simulator with a focus on large-scale satellite networks. We demonstrate its improved capabilities compared to existing offerings, showcase its flexibility and extensibility through an implementation of existing protocols and the DTN simulation reference scenarios recommended by CCSDS, and evaluate its scalability, showing that it exceeds existing tools while providing better fidelity. DSNS provides concrete usefulness to both standards bodies and satellite operators, enabling fast iteration on protocol development and testing of parameters under highly realistic conditions. By removing roadblocks to research and innovation, we can accelerate the development of upcoming satellite networks and ensure that their communication is both fast and secure.

cs.NI

SATversary: Adversarial Attacks and Defenses for Satellite Fingerprinting

Due to the increasing threat of attacks on satellite systems, novel countermeasures have been developed to provide additional security. Among these, there has been a particular interest in transmitter fingerprinting, which authenticates transmitters by looking at characteristics expressed in the physical layer signal. These systems rely heavily upon statistical methods and machine learning, and are therefore vulnerable to a range of attacks. The severity of this threat in a fingerprinting context is currently not well understood. In this paper we evaluate a range of attacks against satellite fingerprinting, building on previous works by looking at attacks optimized to target the fingerprinting system for maximal impact. We design optimized jamming, dataset poisoning, and spoofing attacks, evaluating them in the real world against the SatIQ fingerprinting system designed to authenticate Iridium transmitters, and using a wireless channel emulator to achieve realistic channel conditions. We show that an optimized jamming signal can cause a 50% error rate with attacker-to-victim ratios as low as -30dB (far less power than traditional jamming techniques), and demonstrate successful spoofing attacks, with an attacker successfully removing their own transmitter's fingerprint from messages. We also present a viable dataset poisoning attack, enabling persistent message spoofing by altering stored data to include the fingerprint of the attacker's transmitter. Finally, we show that a model trained to optimize spoofing attacks can also be used to detect spoofing and replay attacks, even when it has never seen the attacker's transmitter before. This technique works even when the training dataset includes only a single transmitter, enabling fingerprinting to be used to protect small constellations and even individual satellites, providing additional protection where it is needed the most.

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OpenSky Report 2025: Improving Crowdsourced Flight Trajectories with ADS-C Data

The OpenSky Network has been collecting and providing crowdsourced air traffic surveillance data since 2013. The network has primarily focused on Automatic Dependent Surveillance--Broadcast (ADS-B) data, which provides high-frequency position updates over terrestrial areas. However, the ADS-B signals are limited over oceans and remote regions, where ground-based receivers are scarce. To address these coverage gaps, the OpenSky Network has begun incorporating data from the Automatic Dependent Surveillance--Contract (ADS-C) system, which uses satellite communication to track aircraft positions over oceanic regions and remote areas. In this paper, we analyze a dataset of over 720,000 ADS-C messages collected in 2024 from around 2,600 unique aircraft via the Alphasat satellite, covering Europe, Africa, and parts of the Atlantic Ocean. We present our approach to combining ADS-B and ADS-C data to construct detailed long-haul flight paths, particularly for transatlantic and African routes. Our findings demonstrate that this integration significantly improves trajectory reconstruction accuracy, allowing for better fuel consumption and emissions estimates. We illustrate how combined data captures flight patterns across previously underrepresented regions across Africa. Despite coverage limitations, this work marks an important advancement in providing open access to global flight trajectory data, enabling new research opportunities in air traffic management, environmental impact assessment, and aviation safety.

cs.SI

OrbID: Identifying Orbcomm Satellite RF Fingerprints

An increase in availability of Software Defined Radios (SDRs) has caused a dramatic shift in the threat landscape of legacy satellite systems, opening them up to easy spoofing attacks by low-budget adversaries. Physical-layer authentication methods can help improve the security of these systems by providing additional validation without modifying the space segment. This paper extends previous research on Radio Frequency Fingerprinting (RFF) of satellite communication to the Orbcomm satellite formation. The GPS and Iridium constellations are already well covered in prior research, but the feasibility of transferring techniques to other formations has not yet been examined, and raises previously undiscussed challenges. In this paper, we collect a novel dataset containing 8992474 packets from the Orbcom satellite constellation using different SDRs and locations. We use this dataset to train RFF systems based on convolutional neural networks. We achieve an ROC AUC score of 0.53 when distinguishing different satellites within the constellation, and 0.98 when distinguishing legitimate satellites from SDRs in a spoofing scenario. We also demonstrate the possibility of mixing datasets using different SDRs in different physical locations.

eess.SP

KeySpace: Enhancing Public Key Infrastructure for Interplanetary Networks

As the use of satellites continues to grow, new networking paradigms are emerging to support the scale and long distance communication inherent to these networks. In particular, interplanetary communication relays connect distant network segments together, but result in a sparsely connected network with long-distance links that are frequently interrupted. In this new context, traditional Public Key Infrastructure (PKI) becomes difficult to implement, due to the impossibility of low-latency queries to a central authority. This paper addresses the challenge of implementing PKI in these complex networks, identifying the essential goals and requirements. Using these requirements, we develop the KeySpace framework, comprising a set of standardized experiments and metrics for comparing PKI systems across various network topologies, evaluating their performance and security. This enables the testing of different protocols and configurations in a standard, repeatable manner, so that improvements can be more fairly tested and clearly demonstrated. We use KeySpace to test two standard PKI protocols in use in terrestrial networks (OCSP and CRLs), demonstrating for the first time that both can be effectively utilized even in interplanetary networks with high latency and frequent interruptions, provided authority is properly distributed throughout the network. Finally, we propose and evaluate a number of novel techniques extending standard OCSP to improve the overhead of connection establishment, reduce link congestion, and limit the reach of an attacker with a compromised key. Using KeySpace we validate these claims, demonstrating their improved performance over the state of the art.

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Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks

We introduce a new family of prompt injection attacks, termed Neural Exec. Unlike known attacks that rely on handcrafted strings (e.g., "Ignore previous instructions and..."), we show that it is possible to conceptualize the creation of execution triggers as a differentiable search problem and use learning-based methods to autonomously generate them. Our results demonstrate that a motivated adversary can forge triggers that are not only drastically more effective than current handcrafted ones but also exhibit inherent flexibility in shape, properties, and functionality. In this direction, we show that an attacker can design and generate Neural Execs capable of persisting through multi-stage preprocessing pipelines, such as in the case of Retrieval-Augmented Generation (RAG)-based applications. More critically, our findings show that attackers can produce triggers that deviate markedly in form and shape from any known attack, sidestepping existing blacklist-based detection and sanitation approaches.

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Secret Collusion among AI Agents: Multi-Agent Deception via Steganography

Recent capability increases in large language models (LLMs) open up applications in which groups of communicating generative AI agents solve joint tasks. This poses privacy and security challenges concerning the unauthorised sharing of information, or other unwanted forms of agent coordination. Modern steganographic techniques could render such dynamics hard to detect. In this paper, we comprehensively formalise the problem of secret collusion in systems of generative AI agents by drawing on relevant concepts from both AI and security literature. We study incentives for the use of steganography, and propose a variety of mitigation measures. Our investigations result in a model evaluation framework that systematically tests capabilities required for various forms of secret collusion. We provide extensive empirical results across a range of contemporary LLMs. While the steganographic capabilities of current models remain limited, GPT-4 displays a capability jump suggesting the need for continuous monitoring of steganographic frontier model capabilities. We conclude by laying out a comprehensive research program to mitigate future risks of collusion between generative AI models.

cs.AI

Sticky Fingers: Resilience of Satellite Fingerprinting against Jamming Attacks

In the wake of increasing numbers of attacks on radio communication systems, a range of techniques are being deployed to increase the security of these systems. One such technique is radio fingerprinting, in which the transmitter can be identified and authenticated by observing small hardware differences expressed in the signal. Fingerprinting has been explored in particular in the defense of satellite systems, many of which are insecure and cannot be retrofitted with cryptographic security. In this paper, we evaluate the effectiveness of radio fingerprinting techniques under interference and jamming attacks, usually intended to deny service. By taking a pre-trained fingerprinting model and gathering a new dataset in which different levels of Gaussian noise and tone jamming have been added to the legitimate signal, we assess the attacker power required in order to disrupt the transmitter fingerprint such that it can no longer be recognized. We compare this to Gaussian jamming on the data portion of the signal, obtaining the remarkable result that transmitter fingerprints are still recognizable even in the presence of moderate levels of noise. Through deeper analysis of the results, we conclude that it takes a similar amount of jamming power in order to disrupt the fingerprint as it does to jam the message contents itself, so it is safe to include a fingerprinting system to authenticate satellite communication without opening up the system to easier denial-of-service attacks.

cs.CR

Assault and Battery: Evaluating the Security of Power Conversion Systems Against Electromagnetic Injection Attacks

Many modern devices, including critical infrastructures, depend on the reliable operation of electrical power conversion systems. The small size and versatility of switched-mode power converters has resulted in their widespread adoption. Whereas transformer-based systems passively convert voltage, switched-mode converters feature an actively regulated feedback loop, which relies on accurate sensor measurements. Previous academic work has shown that many types of sensors are vulnerable to Intentional Electromagnetic Interference (IEMI) attacks, and it has been postulated that power converters, too, are affected. In this paper, we present the first detailed study on switched-mode power converters by targeting their voltage and current sensors through IEMI attacks. We present a theoretical framework for evaluating IEMI attacks against feedback-based power supplies in the general case. We experimentally validate our theoretical predictions by analyzing multiple AC-DC and DC-DC converters, automotive grade current sensors, and dedicated battery chargers, and demonstrate the systematic vulnerability of all examined categories under real-world conditions. Finally, we demonstrate that sensor attacks on power converters can cause permanent damage to Li-Ion batteries during the charging process.

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Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting

Due to an increase in the availability of cheap off-the-shelf radio hardware, spoofing and replay attacks on satellite ground systems have become more accessible than ever. This is particularly a problem for legacy systems, many of which do not offer cryptographic security and cannot be patched to support novel security measures. In this paper we explore radio transmitter fingerprinting in satellite systems. We introduce the SatIQ system, proposing novel techniques for authenticating transmissions using characteristics of transmitter hardware expressed as impairments on the downlinked signal. We look in particular at high sample rate fingerprinting, making fingerprints difficult to forge without similarly high sample rate transmitting hardware, thus raising the budget for attacks. We also examine the difficulty of this approach with high levels of atmospheric noise and multipath scattering, and analyze potential solutions to this problem. We focus on the Iridium satellite constellation, for which we collected 1705202 messages at a sample rate of 25 MS/s. We use this data to train a fingerprinting model consisting of an autoencoder combined with a Siamese neural network, enabling the model to learn an efficient encoding of message headers that preserves identifying information. We demonstrate the system's robustness under attack by replaying messages using a Software-Defined Radio, achieving an Equal Error Rate of 0.120, and ROC AUC of 0.946. Finally, we analyze its stability over time by introducing a time gap between training and testing data, and its extensibility by introducing new transmitters which have not been seen before. We conclude that our techniques are useful for building systems that are stable over time, can be used immediately with new transmitters without retraining, and provide robustness against spoofing and replay by raising the required budget for attacks.

cs.CR

FABRID: Flexible Attestation-Based Routing for Inter-Domain Networks

In its current state, the Internet does not provide end users with transparency and control regarding on-path forwarding devices. In particular, the lack of network device information reduces the trustworthiness of the forwarding path and prevents end-user applications requiring specific router capabilities from reaching their full potential. Moreover, the inability to influence the traffic's forwarding path results in applications communicating over undesired routes, while alternative paths with more desirable properties remain unusable. In this work, we present FABRID, a system that enables applications to forward traffic flexibly, potentially on multiple paths selected to comply with user-defined preferences, where information about forwarding devices is exposed and transparently attested by autonomous systems (ASes). The granularity of this information is chosen by each AS individually, protecting them from leaking sensitive network details, while the secrecy and authenticity of preferences embedded within the users' packets are protected through efficient cryptographic operations. We show the viability of FABRID by deploying it on a global SCION network test bed, and we demonstrate high throughput on commodity hardware.

cs.NI

Perfectly Secure Steganography Using Minimum Entropy Coupling

Steganography is the practice of encoding secret information into innocuous content in such a manner that an adversarial third party would not realize that there is hidden meaning. While this problem has classically been studied in security literature, recent advances in generative models have led to a shared interest among security and machine learning researchers in developing scalable steganography techniques. In this work, we show that a steganography procedure is perfectly secure under Cachin (1998)'s information-theoretic model of steganography if and only if it is induced by a coupling. Furthermore, we show that, among perfectly secure procedures, a procedure maximizes information throughput if and only if it is induced by a minimum entropy coupling. These insights yield what are, to the best of our knowledge, the first steganography algorithms to achieve perfect security guarantees for arbitrary covertext distributions. To provide empirical validation, we compare a minimum entropy coupling-based approach to three modern baselines -- arithmetic coding, Meteor, and adaptive dynamic grouping -- using GPT-2, WaveRNN, and Image Transformer as communication channels. We find that the minimum entropy coupling-based approach achieves superior encoding efficiency, despite its stronger security constraints. In aggregate, these results suggest that it may be natural to view information-theoretic steganography through the lens of minimum entropy coupling.

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Improving Aircraft Localization: Experiences and Lessons Learned from an Open Competition

Knowledge about the exact positioning of aircraft is crucial in many settings. Consequently, the opportunistic and independent localization of aircraft based on their communication has been a longstanding problem and subject of much research. Originating from military settings, the capability to conduct aircraft localization has moved first towards the institutional civil aviation domain and can now be undertaken by anyone who has access to multiple cheap software-defined radios. Based on these technological developments, many crowdsourced sensor networks have sprung up, which collect air traffic control data in order to localize aircraft and visualize the airspace. Due to their unplanned and uncontrolled deployment and heterogeneous receiver technology traditional solutions to the Aircraft Localization Problem (ALP) can either not be applied or do not perform in a satisfactory manner. In order to deal with this issue and to find novel approaches to the ALP itself, we have designed and executed a multi-stage open competition, conducted both offline and online. In this paper, we discuss the setup, experiences, and lessons learned from this Aircraft Localization Competition. We report from a diverse set of technical approaches, comprising 72 participating teams over three stages. The participants reached a localization accuracy of up to 25 meters in a setting with fully GPS-synchronized receivers and 78 meters in a largely unsynchronized receiver setting. These results constitute a significant improvement over the previous baseline used in the OpenSky research network. We compare the results of the study, discuss the current state of the art, and highlight the areas that, based on our experience from organizing a competition, need to be improved for optimal adoption of the competitive approach for other scenarios.

cs.NI

Brokenwire : Wireless Disruption of CCS Electric Vehicle Charging

We present a novel attack against the Combined Charging System, one of the most widely used DC rapid charging technologies for electric vehicles (EVs). Our attack, Brokenwire, interrupts necessary control communication between the vehicle and charger, causing charging sessions to abort. The attack requires only temporary physical proximity and can be conducted wirelessly from a distance, allowing individual vehicles or entire fleets to be disrupted stealthily and simultaneously. In addition, it can be mounted with off-the-shelf radio hardware and minimal technical knowledge. By exploiting CSMA/CA behavior, only a very weak signal needs to be induced into the victim to disrupt communication - exceeding the effectiveness of broadband noise jamming by three orders of magnitude. The exploited behavior is a required part of the HomePlug Green PHY, DIN 70121 & ISO 15118 standards and all known implementations exhibit it. We first study the attack in a controlled testbed and then demonstrate it against eight vehicles and 20 chargers in real deployments. We find the attack to be successful in the real world, at ranges up to 47 m, for a power budget of less than 1 W. We further show that the attack can work between the floors of a building (e.g., multi-story parking), through perimeter fences, and from `drive-by' attacks. We present a heuristic model to estimate the number of vehicles that can be attacked simultaneously for a given output power. Brokenwire has immediate implications for a substantial proportion of the around 12 million battery EVs on the roads worldwide - and profound effects on the new wave of electrification for vehicle fleets, both for private enterprise and crucial public services, as well as electric buses, trucks and small ships. As such, we conducted a disclosure to the industry and discussed a range of mitigation techniques that could be deployed to limit the impact.

cs.CR