共同演化的零日干扰：自适应攻击合成与基于图注意力的在线检测
Co-Evolving Zero-Day Jamming: Adaptive Attack Synthesis and Graph Attention-Based Online Detection
- Arizona State University（亚利桑那州立大学）
- The University of Texas at Arlington（德克萨斯大学阿灵顿分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出双管齐下框架：基于图注意力网络与狄利克雷过程聚类的在线检测器，以及推理驱动的强化学习干扰器，实现零日干扰的检测与攻击合成，分别提升检测准确率20%和攻击效能33%。
AI中文摘要：
对零日干扰检测器的有效评估需要稳健的对抗模型。然而，现有的攻击模型通常假设对目标接收器具有先验知识，这限制了其作为评估基准的实用性。在检测方面，现有检测器无法捕捉干扰行为的全局时频结构，也无法在零日策略出现时对其进行区分。本文通过一个双管齐下的框架来解决这些局限性。首先，引入了一个在线检测框架，该框架将用于时频表示学习的图注意力网络（GAT）与狄利克雷过程（DP）均值聚类相结合。该框架在统一的学习目标下联合分类已知策略并发现零日策略。其次，提出了一种推理驱动的强化学习（RL）干扰器作为对抗基准。该干扰器将目标接收器视为黑盒，通过假设检验推断检测器状态，并优化攻击影响与隐蔽性之间的权衡。仿真结果表明，所提出的RL干扰器优于基准，实现了33%更高的攻击效能和67%更高的隐蔽性。所提出的检测框架针对所提出的RL干扰器，其检测准确率比基准高20%。
英文摘要：
Effective evaluation of zero-day jamming detectors requires robust adversarial models. However, existing attack models often assume prior knowledge of the target receiver, limiting their utility as evaluation benchmarks. On the detection side, existing detectors fail to capture the global temporal-spectral structure of jamming behavior and cannot differentiate zero-day strategies as they emerge. This paper addresses these limitations through a two-pronged framework. First, an online detection framework is introduced that combines a graph attention network (GAT) for temporal-spectral representation learning with Dirichlet process (DP)-means clustering. This framework jointly classifies known and discovers zero-day strategies within a unified learning objective. Second, an inference-driven reinforcement learning (RL) jammer is proposed as an adversarial benchmark. The jammer treats the target receiver as a black-box, infers the detector state via hypothesis testing, and optimizes the trade-off between attack impact and stealth. Simulation results show that the proposed RL jammer outperforms benchmarks, achieving 33% higher attack efficacy and 67% higher stealth. The proposed detection framework against the proposed RL jammer is shown to achieve 20% higher detection accuracy than the benchmarks.
