JANUS：针对低地球轨道卫星网络中跳波束的拒绝服务攻击
JANUS: Denial-of-Service Attack Against Beam Hopping in LEO Satellite Networks
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中文总结 AI 辅助
本文提出JANUS，一种针对LEO卫星网络跳波束系统的定向拒绝服务攻击，通过僵尸网络注入流量操纵调度器，实现对约95%受害者的服务拒绝，并评估了缓解策略。
中文摘要 AI 辅助
低地球轨道（LEO）卫星网络日益被用于提供全球连接。然而，每颗卫星的资源有限，需要根据随地理和时间变化的需求进行分配。跳波束通过将卫星的服务区域划分为地理单元来应对这一挑战。它不是同时照亮每个单元，而是根据需求动态地将可用波束分配给选定的子集。这种对观测到的流量需求作为波束选择决策输入的依赖，创造了一个新的攻击面，其安全影响很少受到关注。在本文中，我们提出了JANUS，一种针对LEO网络中跳波束系统的新型定向拒绝服务攻击。我们证明，一个由受损终端组成的小型僵尸网络可以向精心选择的非受害者单元注入合法用户流量，以操纵跳波束调度器对需求的视图。这种操纵改变了波束分配决策，并将服务从目标受害者区域转移开。我们在不同的系统配置、调度器、攻击时域和攻击者知识设置下评估JANUS，以刻画攻击的有效性、所需资源以及随时间推移导致的服务中断。针对基于排名的KMAX调度器，JANUS在多达约95%的评估受害者中实现了完全服务拒绝。针对DRL，JANUS可以将受害者排除在约92%的调度决策之外。最后，我们评估了降低攻击有效性的缓解策略。
英文摘要
Low Earth orbit (LEO) satellite networks are increasingly used to provide global connectivity. However, each satellite has limited resources that need to be allocated according to demand, which varies geographically and over time. Beam hopping addresses this challenge by dividing a satellite's service area into geographic cells. Rather than illuminating every cell simultaneously, it dynamically assigns available beams to a selected subset based on demand. This reliance on observed traffic demand as an input to beam-selection decisions creates a new attack surface whose security implications have received little attention. In this paper, we present JANUS, a novel targeted denial-of-service attack against beam-hopping systems in LEO networks. We show that a small botnet of compromised terminals can inject legitimate user traffic into carefully selected non-victim cells to manipulate the beam-hopping scheduler's view of demand. This manipulation alters beam-allocation decisions and redirects service away from the targeted victim area. We evaluate JANUS across different system configurations, schedulers, attack horizons, and attacker-knowledge settings to characterize the attack's effectiveness, required resources, and resulting service disruption over time. Against a rank-based KMAX scheduler, JANUS achieves complete service denial for up to approximately 95% of evaluated victims. Against DRL, JANUS can exclude the victim from approximately 92% of scheduling decisions. Finally, we evaluate mitigation strategies that reduce the attack effectiveness.
