JANUS: Denial-of-Service Attack Against Beam Hopping in LEO Satellite Networks

arXiv:2609.19977 · cs.CR · Submitted 2026-09-17 · Read on arXiv

cs.CR

Submitted: 2026-09-17

Updated: 2026-09-17

Comments: Yuval Aviv and Roee Idan contributed equally to this work

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Reinforcement Learning — 11× in this paper · explained in 4 episodes
• Deep Reinforcement Learning — 8× in this paper · explained in 2 episodes
• Robustness — 6× in this paper · explained in 5 episodes
• Actor — 2× in this paper · explained in 3 episodes
• Adaptability — 2× in this paper · explained in 1 episode
• Adversarial Attacks — 2× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Action Space — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adversarial Robustness — 1× in this paper · explained in 3 episodes
• Culture-Oriented Science and Technology — 1× in this paper
• Deep Reinforcement Learning (DRL) — 1× in this paper · explained in 2 episodes
• Digital Twin — 1× in this paper · explained in 4 episodes
• European Telecommunications Standards Institute — 1× in this paper
• Exponential Moving Average (EMA) — 1× in this paper · explained in 2 episodes
• Generalization — 1× in this paper · explained in 13 episodes
• Multi-objective Optimization — 1× in this paper · explained in 4 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Steering — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Two-Line Element — 1× in this paper

Sources

• Multi-Satellite Beam Hopping and Power Allocation Using Deep Reinforcement Learning
• HYDRA: Quantifying Botnet Resource Thresholds for Efficient Link-Flooding Attacks on LEO Satellite Networks
• Adversarial Attacks on Neural Network Policies
• Tactics of Adversarial Attack on Deep Reinforcement Learning Agents
• Adversarial Policies: Attacking Deep Reinforcement Learning

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