Alexander Kedrowitsch (Virginia Tech), Jonathan Black (Virginia Tech) Daphne Yao (Virginia Tech)

Inter-satellite links will unlock true global access to high-speed internet delivered by Low Earth Orbit (LEO) mega-constellations. Functional packet routing within the constraints of the space environment, spacecraft design, and continual satellite mobility is uniquely challenging and requires novel routing algorithm approaches. Additionally, recent real-world events have highlighted adversarial attempts to deny and disrupt mega-constellation networking capabilities. In this paper, we advance highly resilient LEO mega-constellation dynamic routing algorithms by presenting our novel, ISL architecture-derived, network coordinate system. This coordinate system simplifies the network topology and permits increasingly impactful routing decisions with minimal computational overhead. From our topology, we demonstrate a proof-of-concept, lightweight routing algorithm that is highly resilient and scalable. To promote standardized resilience comparisons for LEO mega-constellation routing algorithms, we also propose a routing resilience testing framework that defines key performance metrics, adversarial capabilities, and testing scenarios. Using our proposed framework, we demonstrate our routing algorithm’s increased resilience over several state-of-the-art dynamic routing algorithms, with 12% packet delivery rate improvement during high adversarial disruption intensities.

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Gradient Shaping: Enhancing Backdoor Attack Against Reverse Engineering

Rui Zhu (Indiana University Bloominton), Di Tang (Indiana University Bloomington), Siyuan Tang (Indiana University Bloomington), Zihao Wang (Indiana University Bloomington), Guanhong Tao (Purdue University), Shiqing Ma (University of Massachusetts Amherst), XiaoFeng Wang (Indiana University Bloomington), Haixu Tang (Indiana University, Bloomington)

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AdvCAPTCHA: Creating Usable and Secure Audio CAPTCHA with Adversarial...

Hao-Ping (Hank) Lee (Carnegie Mellon University), Wei-Lun Kao (National Taiwan University), Hung-Jui Wang (National Taiwan University), Ruei-Che Chang (University of Michigan), Yi-Hao Peng (Carnegie Mellon University), Fu-Yin Cherng (National Chung Cheng University), Shang-Tse Chen (National Taiwan University)

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GraphGuard: Detecting and Counteracting Training Data Misuse in Graph...

Bang Wu (CSIRO's Data61/Monash University), He Zhang (Monash University), Xiangwen Yang (Monash University), Shuo Wang (CSIRO's Data61/Shanghai Jiao Tong University), Minhui Xue (CSIRO's Data61), Shirui Pan (Griffith University), Xingliang Yuan (Monash University)

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Stacking up the LLM Risks: Applied Machine Learning Security

Dr. Gary McGraw, Berryville Institute of Machine Learning

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