Muaz Ali

Muaz Ali, Utkarsh Upadhyay, Sean McCormick et al.

Starlink has rapidly emerged as the world's largest satellite constellation and the de facto reference system for low Earth orbit (LEO) networking research. Existing literature predominantly models Starlink as a static, symmetric, and fully deployed structure with uniformly distributed satellites. However, we reveal that Starlink's actual deployment, orbital configurations, and operational dynamics fundamentally deviate from these idealized assumptions. Leveraging satellite observation data spanning 2019 to 2025, we demonstrate that the constellation is highly dynamic across multiple temporal and spatial scales. Macroscopically, Starlink comprises multiple orbital shells undergoing continuous active deployment and reconfiguration. Microscopically, individual satellites exhibit high mobility, frequently executing collision-avoidance maneuvers, altitude adjustments, and intra-orbital relocations. We discover that while the majority of satellites form a relatively stable structure with near-uniform spacing, other satellites tend to cluster as twins or triads as in-orbit backups. Furthermore, empirical survival analysis indicates an operational lifespan of 4-6 years and an average daily failure probability of 0.0128%. Ultimately, our data-driven characterization exposes Starlink as a highly heterogeneous and continuously evolving network. We provide critical empirical insights that challenge prevailing simulation models, offering a more accurate foundation for future LEO topology design, routing protocols, and performance evaluations.

Gabriel Sauger, Jean-Yves Marion, Sazzadur Rahaman et al.

Binary function classifiers play a crucial role in maintaining the security and integrity of software systems by detecting malicious code and unauthorized modifications. However, machine learning-based classifiers are vulnerable to adversarial attacks that can evade detection. In this study, we present Kelpie, a novel framework for executing mimicry attacks, a stronger type of targeted evasion attacks, on binary function classifiers in a black-box, zero-query setting. Unlike previous approaches that rely on querying the target classifier to refine untargeted evasion attacks, Kelpie leverages code transformations that preserve the functionality of malicious payloads while causing them to be misclassified as we want. Through extensive experimentation, we demonstrate that Kelpie can successfully execute mimicry attacks against six state-of-the-art binary function classifiers representing different model architectures without requiring direct interaction with them. We further validate our approach with a practical demonstration, involving a keylogger and a wiper concealed within benign-looking functions embedded in an application. This work, to our best knowledge, is the first to demonstrate such a mimicry attack in a black-box, zero-query context, raising important questions about the reliability and security of existing machine learning-based binary function classifiers.