Suchergebnisse

Despite the Aircraft Communications, Addressing and Reporting System (ACARS) being widely deployed for over twenty years, little scrutiny has been applied to it outside of the aviation community. Whilst originally utilized by commercial airlines to track their flights and provide automated timekeeping on crew, today it serves as a multi-purpose air-ground data link for many aviation stakeholders including private jet owners, state actors and military. Such a change has caused ACARS to be used far beyond its original mandate; to date no work has been undertaken to assess the extent of this especially with regard to privacy and the various stakeholder groups which use it. In this paper, we present an analysis of ACARS usage by privacy sensitive actors-military, government and business. We conduct this using data from the VHF (both traditional ACARS, and VDL mode 2) and satellite communications subnetworks. Based on more than two million ACARS messages collected over the course of 16 months, we demonstrate that current ACARS usage systematically breaches location privacy for all examined aviation stakeholder groups, explaining the types of messages used to cause this problem.We illustrate the challenges with three case studies-one for each stakeholder group-to show how much privacy sensitive information can be constructed with a handful of ACARS messages. We contextualize our findings with opinions on the issue of privacy in ACARS from 40 aviation industry professionals. From this, we explore recommendations for how to address these issues, including use of encryption and policy measures. ; ISSN:2299-0984

Attacks on behavioral biometrics have become increasingly popular. Most research has been focused on presenting a previously obtained feature vector to the biometric sensor, often by the attacker training themselves to change their behavior to match that of the victim. However, obtaining the victim's biometric information may not be easy, especially when the user's template on the authentication device is adequately secured. As such, if the authentication device is inaccessible, the attacker may have to obtain data elsewhere. In this paper, we present an analytic framework that enables us to measure how easily features can be predicted based on data gathered in a different context (e.g., different sensor, performed task or environment). This framework is used to assess how resilient individual features or entire biometrics are against such cross-context attacks. In order to be able to compare existing biometrics with regard to this property, we perform a user study to gather biometric data from 30 participants and five biometrics (ECG, eye movements, mouse movements, touchscreen dynamics and gait) in a variety of contexts. We make this dataset publicly available online. Our results show that many attack scenarios are viable in practice as features are easily predicted from a variety of contexts. All biometrics include features that are particularly predictable (e.g., amplitude features for ECG or curvature for mouse movements). Overall, we observe that cross-context attacks on eye movements, mouse movements and touchscreen inputs are comparatively easy while ECG and gait exhibit much more chaotic cross-context changes. ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 722022