The paper develops a novel, resource-aware cybersecurity risk assessment framework specifically tailored for power-limited CubeSat missions, demonstrating that adapting controls can significantly impr…

This paper analyzes the latency-accuracy trade-offs of various TinyML models for detecting diverse cyber-RF threats on autonomous spacecraft, finding that Logistic Regression offers an effective, low-…

This paper proposes and analyzes architectural designs for space-based Public Key Infrastructure (PKI) to enable secure, low-latency authentication and trust services for rapidly expanding satellite c…

The paper proposes a cross-layer behavioral fingerprinting framework that fuses physical and network data to detect comprehensive attacks in dense LEO satellite constellations, achieving high detectio…

The paper introduces Glass Box, a runtime constitutional AI verification layer designed to ensure the safety and adherence to physical laws for autonomous AI systems operating in orbital data centers.

The paper proposes a proactive, resilient architecture for autonomous vehicles by integrating redundancy, diversity, and adaptive reconfiguration to defend against various cyber and physical attacks.

Space Fabric introduces a novel satellite-based Trusted Execution Architecture (TEE) that establishes trust for orbital computing by generating cryptographic secrets and binding workload execution to…

This Survey of Knowledge (SoK) identifies a disconnect between academic NIDS research and real-world operational contexts, proposing foundational changes to reshape future research.

This paper enhances anomaly detection and threat intelligence in Zero Trust IoT environments by applying and comparing various machine learning classifiers, notably using SMOTE to improve accuracy on…

This paper enhances an existing autonomous online Intrusion Detection System (AOC-IDS) for IoT by addressing class imbalance, pseudo-label reliability, and computational overhead, achieving significan…

Analyzing 10 days of global internet traffic from a network telescope reveals that a small fraction of source IPs dominate traffic, with a notable focus on exploiting legacy IoT devices via Telnet por…

This paper proposes a cloud-edge-end collaborative defense framework to secure UAV swarms against various threats like GPS spoofing and multi-hop intrusions, demonstrating its effectiveness through ex…

SentinelSphere is an AI platform that integrates advanced deep learning for real-time threat detection with an LLM-powered training system to holistically address both technical and human-factor cyber…

The paper introduces the concept of 'host-space perturbations,' arguing that real-world attackers can only manipulate network inputs by controlling specific hosts, a constraint that significantly weak…

The paper introduces a threat-oriented digital twinning methodology to enable reproducible and controllable cybersecurity evaluation of autonomous platforms, overcoming limitations in accessing real-w…

This paper discusses the significant challenges in developing a holistic intrusion detection system for Industrial Control Systems (ICS) that must cover all operational dimensions.

The paper proposes and evaluates efficient, quantum-safe variants of the Internet Key Exchange (IKE) protocol tailored for the unique resource constraints and latency challenges of satellite communica…

The paper introduces COBALT, a Z3 SMT-based formal verification engine, to proactively detect arithmetic vulnerabilities (CWE-190/191/195) in the critical infrastructure surrounding frontier AI models…

The paper introduces an end-to-end framework that not only detects network intrusions using deep learning but also generates actionable, citation-grounded mitigation reports using a Retrieval-Augmente…

The paper proposes an end-to-end LLM framework that automates SOC operations by integrating ensemble-based threat detection, syntax-constrained query generation, and evidence-grounded incident resolut…