arXiv subjects

Ivan Martinovic

Publications and source records attributed to Ivan Martinovic.

At least 19 recordsLinked to original sources

arXiv2026

Finding Phones Fast: Low-Latency and Scalable Monitoring of Cellular Communications in Sensitive Areas

The widespread availability of cellular devices introduces new threat vectors that allow users or attackers to bypass security policies and physical barriers and bring unauthorized devices into sensitive areas. We identify a critical gap in this context: the absence of low-latency systems for high-quality and instantaneous monitoring of cellular transmissions. Such low-latency systems are crucial to allow for timely detection, decision, and disruption of unauthorized communication in sensitive areas. Operator-based monitoring systems, built for purposes such as people counting or tracking, lack real-time capability, require cooperation across multiple operators, and thus are hard to deploy. Operator-independent monitoring approaches proposed in the literature either lack low-latency capabilities or do not scale. We propose WaveTag, the first low-latency and scalable system designed to monitor 5G and LTE connections across all operators prior to any user data transmission. WaveTag consists of several downlink sniffers and a distributed network of uplink sniffers that measure both downlink protocol information and uplink signal characteristics at multiple locations to gain a detailed spatial image of uplink signals. WaveTag then aggregates the recorded information, processes it, and provides a decision about the connection--all done prior to the complete connection establishment of a UE. To evaluate WaveTag, we deployed it in the context of geofencing, where WaveTag was able to determine whether the signals originate from inside or outside of an area within 2.3 ms of the initial base station-to-device message, therefore enabling prompt and targeted suppression of communication before any user data was transmitted. WaveTag achieved 99.66% geofencing classification accuracy. Finally, we conduct a real-world uplink measurement evaluation on a commercial 5G SA network.

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arXiv2026

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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arXiv2026

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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arXiv2026

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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arXiv2025

VET Your Agent: Towards Host-Independent Autonomy via Verifiable Execution Traces

Recent advances in large language models (LLMs) have enabled a new generation of autonomous agents that operate over sustained periods and manage sensitive resources on behalf of users. Trusted for their ability to act without direct oversight, such agents are increasingly considered in high-stakes domains including financial management, dispute resolution, and governance. Yet in practice, agents execute on infrastructure controlled by a host, who can tamper with models, inputs, or outputs, undermining any meaningful notion of autonomy. We address this gap by introducing VET (Verifiable Execution Traces), a formal framework that achieves host-independent authentication of agent outputs and takes a step toward host-independent autonomy. Central to VET is the Agent Identity Document (AID), which specifies an agent's configuration together with the proof systems required for verification. VET is compositional: it supports multiple proof mechanisms, including trusted hardware, succinct cryptographic proofs, and notarized TLS transcripts (Web Proofs). We implement VET for an API-based LLM agent and evaluate our instantiation on realistic workloads. We find that for today's black-box, secret-bearing API calls, Web Proofs appear to be the most practical choice, with overhead typically under 3$\times$ compared to direct API calls, while for public API calls, a lower-overhead TEE Proxy is often sufficient. As a case study, we deploy a verifiable trading agent that produces proofs for each decision and composes Web Proofs with a TEE Proxy. Our results demonstrate that practical, host-agnostic authentication is already possible with current technology, laying the foundation for future systems that achieve full host-independent autonomy.

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arXiv2025

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.

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arXiv2025

Current Affairs: A Security Measurement Study of CCS EV Charging Deployments

Since its introduction in 2012, the Combined Charging System (CCS) has emerged as the leading technology for EV fast charging in Europe, North America and parts of Asia. The charging communication of CCS is defined by the ISO 15118 standards, which have been improved over the years. Most notably, in 2014, important security features such as Transport Layer Security (TLS) and usability enhancements such as Plug and Charge were introduced. In this paper, we conduct the first measurement study of publicly deployed CCS DC charging stations to capture the state of deployment for different protocol versions and to better understand the attack surface of the EV charging infrastructure. In our evaluation, we examine 325 chargers manufactured between April 2013 and June 2023, and installed as late as May 2024 by 26 manufacturers across 4 European countries. We find that only 12% of the charging stations we analyzed implement TLS at all, leaving all others vulnerable to attacks that have already been demonstrated many years ago. We observe an increasing trend in support for ISO 15118-2 over the years, reaching 70% of chargers manufactured in 2023. We further notice that most chargers use a decade-old firmware for their HomePlug modems, which could contain vulnerabilities that have been patched since. Finally, we discuss design flaws with the Public Key Infrastructure system used in EV charging, and propose changes to improve the adoption and availability of TLS.

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arXiv2024

A Mobile Payment Scheme Using Biometric Identification with Mutual Authentication

Cashless payment systems offer many benefits over cash, but also have some drawbacks. Fake terminals, skimming, wireless connectivity, and relay attacks are persistent problems. Attempts to overcome one problem often lead to another - for example, some systems use QR codes to avoid skimming and connexion issues, but QR codes can be stolen at distance and relayed. In this paper, we propose a novel mobile payment scheme based on biometric identification that provides mutual authentication to protect the user from rogue terminals. Our scheme imposes only minimal requirements on terminal hardware, does not depend on wireless connectivity between the user and the verifier during the authentication phase, and does not require the user to trust the terminal until it has authenticated itself to the user. We show that our scheme is resistant against phishing, replay, relay, and presentation attacks.

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arXiv2024

UserBoost: Generating User-specific Synthetic Data for Faster Enrolment into Behavioural Biometric Systems

Behavioural biometric authentication systems entail an enrolment period that is burdensome for the user. In this work, we explore generating synthetic gestures from a few real user gestures with generative deep learning, with the application of training a simple (i.e. non-deep-learned) authentication model. Specifically, we show that utilising synthetic data alongside real data can reduce the number of real datapoints a user must provide to enrol into a biometric system. To validate our methods, we use the publicly available dataset of WatchAuth, a system proposed in 2022 for authenticating smartwatch payments using the physical gesture of reaching towards a payment terminal. We develop a regularised autoencoder model for generating synthetic user-specific wrist motion data representing these physical gestures, and demonstrate the diversity and fidelity of our synthetic gestures. We show that using synthetic gestures in training can improve classification ability for a real-world system. Through this technique we can reduce the number of gestures required to enrol a user into a WatchAuth-like system by more than 40% without negatively impacting its error rates.

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arXiv2024

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.

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arXiv2024

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.

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arXiv2023

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.

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arXiv2023

POSTER: spaceQUIC: Securing Communication in Computationally Constrained Spacecraft

Recent years have seen a rapid increase in the number of CubeSats and other small satellites in orbit - these have highly constrained computational and communication resources, but still require robust secure communication to operate effectively. The QUIC transport layer protocol is designed to provide efficient communication with cryptography guarantees built-in, with a particular focus on networks with high latency and packet loss. In this work we provide spaceQUIC, a proof of concept implementation of QUIC for NASA's "core Flight System" satellite operating system, and assess its performance.

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arXiv2023

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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arXiv2023

RADAR: A TTP-based Extensible, Explainable, and Effective System for Network Traffic Analysis and Malware Detection

Network analysis and machine learning techniques have been widely applied for building malware detection systems. Though these systems attain impressive results, they often are $(i)$ not extensible, being monolithic, well tuned for the specific task they have been designed for but very difficult to adapt and/or extend to other settings, and $(ii)$ not interpretable, being black boxes whose inner complexity makes it impossible to link the result of detection with its root cause, making further analysis of threats a challenge. In this paper we present RADAR, an extensible and explainable system that exploits the popular TTP (Tactics, Techniques, and Procedures) ontology of adversary behaviour described in the industry-standard MITRE ATT\&CK framework in order to unequivocally identify and classify malicious behaviour using network traffic. We evaluate RADAR on a very large dataset comprising of 2,286,907 malicious and benign samples, representing a total of 84,792,452 network flows. The experimental analysis confirms that the proposed methodology can be effectively exploited: RADAR's ability to detect malware is comparable to other state-of-the-art non-interpretable systems' capabilities. To the best of our knowledge, RADAR is the first TTP-based system for malware detection that uses machine learning while being extensible and explainable.

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arXiv2023

Dishing Out DoS: How to Disable and Secure the Starlink User Terminal

Satellite user terminals are a promising target for adversaries seeking to target satellite communication networks. Despite this, many protections commonly found in terrestrial routers are not present in some user terminals. As a case study we audit the attack surface presented by the Starlink router's admin interface, using fuzzing to uncover a denial of service attack on the Starlink user terminal. We explore the attack's impact, particularly in the cases of drive-by attackers, and attackers that are able to maintain a continuous presence on the network. Finally, we discuss wider implications, looking at lessons learned in terrestrial router security, and how to properly implement them in this new context.

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arXiv2023

FETA: Fair Evaluation of Touch-based Authentication

In this paper, we investigate common pitfalls affecting the evaluation of authentication systems based on touch dynamics. We consider different factors that lead to misrepresented performance, are incompatible with stated system and threat models or impede reproducibility and comparability with previous work. Specifically, we investigate the effects of (i) small sample sizes (both number of users and recording sessions), (ii) using different phone models in training data, (iii) selecting non-contiguous training data, (iv) inserting attacker samples in training data and (v) swipe aggregation. We perform a systematic review of 30 touch dynamics papers showing that all of them overlook at least one of these pitfalls. To quantify each pitfall's effect, we design a set of experiments and collect a new longitudinal dataset of touch interactions from 515 users over 31 days comprised of 1,194,451 unique strokes. Part of this data is collected in-lab with Android devices and the rest remotely with iOS devices, allowing us to make in-depth comparisons. We make this dataset and our code available online. Our results show significant percentage-point changes in reported mean EER for several pitfalls: including attacker data (2.55%), non-contiguous training data (3.8%) and phone model mixing (3.2%-5.8%). We show that, in a common evaluation setting, the cumulative effects of these evaluation choices result in a combined difference of 8.9% EER. We also largely observe these effects across the entire ROC curve. The pitfalls are evaluated on four distinct classifiers - SVM, Random Forest, Neural Network, and kNN. Furthermore, we explore additional considerations for fair evaluation when building touch-based authentication systems and quantify their impacts. Based on these insights, we propose a set of best practices that, will lead to more realistic and comparable reporting of results in the field.

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arXiv2022

WatchAuth: User Authentication and Intent Recognition in Mobile Payments using a Smartwatch

In this paper, we show that the tap gesture, performed when a user 'taps' a smartwatch onto an NFC-enabled terminal to make a payment, is a biometric capable of implicitly authenticating the user and simultaneously recognising intent-to-pay. The proposed system can be deployed purely in software on the watch without requiring updates to payment terminals. It is agnostic to terminal type and position and the intent recognition portion does not require any training data from the user. To validate the system, we conduct a user study (n=16) to collect wrist motion data from users as they interact with payment terminals and to collect long-term data from a subset of them (n=9) as they perform daily activities. Based on this data, we identify optimum gesture parameters and develop authentication and intent recognition models, for which we achieve EERs of 0.08 and 0.04, respectively.

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