10 papers
Herding End-to-End Autonomous Driving via Neuro-Symbolic Safety Guards
Simón Patiño Idarraga, Erick Silva, Rehana Yasmin +1
Modern end-to-end driving agents can achieve high average performance yet still violate basic traffic rules that a human driver would never miss. The reason is structural: they lea…
FedRings: A Scalable and Topology-Aware Federated Learning Framework for LEO Satellite Constellations
Ziwu Liu, Inês Pinto Gouveia, Inês Pinto Gouveia +3
Federated learning over low Earth orbit (LEO) satellite networks is limited by frequent link changes, short contact times, and a highly dynamic topology, making centralized or sync…
Scalable Malware Family Classification Using Quantum Kernel Based Machine Learning
Ratun Rahman, Hassan Jalil Hadi, Christopher Gabriel Pedraza Pohlenz +1
The classification of malware families is a key challenge in cybersecurity, which enables threat attribution, analysis of attack operations, and the formulation of effective defens…
GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks
Hassan Jalil Hadi, Rehana Yasmin, Ali Shoker
Rule-based Intrusion Detection and Prevention Systems (IDPS) offer precise attack detection as well as mitigation, however their manually crafted, signature-driven rules limit adap…
Toward Space-Based Public Key Systems: Enabling Secure Space Communications through In-Orbit Trust Services
Rehana Yasmin, Paulo Esteves-Verissimo, Ali Shoker
The New Space era has led to a rapid increase in satellites operated by independent entities in near-Earth orbit. This shift enables richer space services but also requires secure,…
LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution
Christopher G. Pedraza Pohlenz, Hassan Jalil Hadi, Ali Hassan +1
LLMs are increasingly explored for malware analysis; however, current LLM-based malware attribution remains limited by unsupported indicators and insufficient code-level grounding…