刻画方差包络：跨架构与工作负载的Spectre遥测多维分析
Characterizing the Variance Envelope: A Multi-Dimensional Analysis of Spectre Telemetry Across Architectures and Workloads
AI总结：
本文刻画Spectre攻击的方差包络，通过跨Intel、ARM、AMD架构的实验，发现HPC特征易受环境扭曲、Prime+Probe在AMD Jaguar上存在硬件瓶颈，提出需架构感知的自适应监控用于Spectre检测。
AI中文摘要：
Spectre这类硬件攻击会利用处理器内置漏洞，在硬件性能计数器（HPC）指标中留下异常足迹。尽管机器学习可在受控环境中检测这些足迹，但静态模型在真实场景中会因系统背景噪声、多样攻击变体及对抗性流量 pacing 而失效。为弥合此差距，本文刻画了“方差包络”——即攻击特征偏移的全范围——覆盖Intel、ARM和AMD架构。我们评估了包含3种攻击变体、4种pacing模式及4种背景噪声条件的广泛实验矩阵。分析证明HPC特征高度脆弱，易被执行环境扭曲；此外，我们揭示了一个关键微架构瓶颈：Prime+Probe攻击在AMD Jaguar上持续存在硬件级失效。最终，此全面刻画表明，可靠的运行时检测需架构感知的自适应监控，而非静态模型。
英文摘要：
Hardware attacks like Spectre exploit built-in processor vulnerabilities, leaving anomalous footprints in Hardware Performance Counter (HPC) metrics. While machine learning can detect these footprints in controlled settings, static models fail in the real world when confronted with background system noise, diverse attack variants, and adversarial traffic pacing. To close this gap, this paper characterizes the "variance envelope"-the full range of how attack signatures shift- across Intel, ARM, and AMD architectures. We evaluate an extensive experimental matrix encompassing three attack variants, four pacing modes, and four background-noise conditions. Our analysis proves that HPC signatures are highly fragile and easily warped by their execution environment. Furthermore, we expose a critical microarchitectural bottleneck: the persistent, hardware-level failure of Prime+Probe attacks on the AMD Jaguar. Ultimately, this comprehensive characterization demonstrates that reliable runtime detection requires architecture-aware, adaptive monitoring rather than static models.
