` 包裹，长文本用 `
`/`
` 划分小节。 3. 列表使用 `
- `，引用使用 `
`，代码使用 `
`，重要句子使用 ``。
4. 保留原文所有信息，无增删改。
5. 不输出 ``/``/`` 标签。
2. 分析输入文本：
* *第一段：* Cyber-physical smart-grid detectors are typically treated as black boxes and validated exclusively on clean, temporally shuffled datasets, which risk overly optimistic performance claims. This work presents a multi-testbed evaluation study built around an interpretable, time-windowed fusion Random Forest, adopting the pessimistic result whenever evaluation protocols disagree.
* *项目列表（核心发现）：*
* (i) On a public paired cyber-physical dataset, shuffled cross-validation is optimistic: macro-F1 falls from 0.96 to 0.88 +/- 0.06 under rolling-origin temporal evaluation. A topology-free graph convolutional network (GCN) serving as a control confirms that this lightweight model is competitive on these splits.
* (ii) The adaptive and adaptive-joint feature-space attacks, which are infeasible as realistic grid manipulations, degrade macro-F1 to 0.67 and 0.34, respectively. Adversarial training and randomized smoothing partially restore performance, and a physics-constrained evolution-strategy evasion on the IEEE 14-Bus testbed shows the same vulnerability pattern.
* (iii) On real IEC-104 traffic, the cyber view reliably catches structural attacks (recall of 0.87 to 0.98) but largely fails on semantic value manipulation (recall of 0.11 to 0.35). An observability taxonomy on the IEEE 14-Bus system reveals that stealthy false data injection defeats all three observation views.
* (iv) On a full AC weighted least-squares state-estimation testbed, a forecasting-aided temporal test is evaded by slow ramps precisely where a rate-invariant physics-manifold test sustains detection at a common 5% false-alarm target. A full end-to-end evaluation on IEEE 118-Bus confirms the ordering but bounds its strength: the manifold test detects every trial down to 0.05 degree/step and 0.48 at 0.02 degree/step, against 0.14 for the temporal test. All code and benchmarks are released.
3. 规划 HTML 结构：
* 输入文本有一个开头段落，然后是四个要点 (i, ii, iii, iv)。我可以将这四个要点分组在一个简短的 `` 下，形成一个结构良好的专业报告（根据规则 2：“长文用
/
划分小节”）。
* 让我们添加一个 `
` 作为主要章节标题，例如 “研究概述与核心发现” 或者契合新闻主题的标题。参考文本中的措辞，最准确的可能是 “研究背景与核心发现”，因为它是工作内容里的明确短语。
* 我将使用 `
` “实验结果与评估” 作为四个要点列表的标题，该标题与文本非常契合。
* 列表中的所有文本都需要严格保留。我会将列表项包裹在 `` 中。
* 我应该为第一句或核心结论添加 ``。规则说“重要句子可用”。 * 第一段：“Cyber-physical smart-grid detectors are typically treated as black boxes and validated exclusively on clean, temporally shuffled datasets, which risk overly optimistic performance claims.” 推断为重要句子，用 `` 包裹。 * 第 (i) 点：“shuffled cross-validation is optimistic” 用 `` 包裹。 * 第 (ii) 点：“degrade macro-F1 to 0.67 and 0.34” 用 `` 包裹。 * 第 (iii) 点：“the cyber view reliably catches structural attacks (recall of 0.87 to 0.98) but largely fails on semantic value manipulation (recall of 0.11 to 0.35)” 用 `` 包裹。 * 第 (iv) 点：“the manifold test detects every trial down to 0.05 degree/step and 0.48 at 0.02 degree/step, against 0.14 for the temporal test” 用 `` 包裹。 * 检查特殊字符：`0.88 +/- 0.06`，`IEEE 14-Bus`，`IEC-104`，`0.05 degree/step`。没有 CSS 转义的字符。文本极其干净。 * 确保不添加任何Markdown代码块（```html …… ```）或外部评论。 4. 构建 HTML： ```html
研究背景与核心发现
Cyber-physical smart-grid detectors are typically treated as black boxes and validated exclusively on clean, temporally shuffled datasets, which risk overly optimistic performance claims. This work presents a multi-testbed evaluation study built around an interpretable, time-windowed fusion Random Forest, adopting the pessimistic result whenever evaluation protocols disagree.
实验结果与评估
- (i) On a public paired cyber-physical dataset, shuffled cross-validation is optimistic: macro-F1 falls from 0.96 to 0.88 +/- 0.06 under rolling-origin temporal evaluation. A topology-free graph convolutional network (GCN) serving as a control confirms that this lightweight model is competitive on these splits.
- (ii) The adaptive and adaptive-joint feature-space attacks, which are infeasible as realistic grid manipulations, degrade macro-F1 to 0.67 and 0.34, respectively. Adversarial training and randomized smoothing partially restore performance, and a physics-constrained evolution-strategy evasion on the IEEE 14-Bus testbed shows the same vulnerability pattern.
- (iii) On real IEC-104 traffic, the cyber view reliably catches structural attacks (recall of 0.87 to 0.98) but largely fails on semantic value manipulation (recall of 0.11 to 0.35). An observability taxonomy on the IEEE 14-Bus system reveals that stealthy false data injection defeats all three observation views.
- (iv) On a full AC weighted least-squares state-estimation testbed, a forecasting-aided temporal test is evaded by slow ramps precisely where a rate-invariant physics-manifold test sustains detection at a common 5% false-alarm target. A full end-to-end evaluation on IEEE 118-Bus confirms the ordering but bounds its strength: the manifold test detects every trial down to 0.05 degree/step and 0.48 at 0.02 degree/step, against 0.14 for the temporal test. All code and benchmarks are released.
``` 5. 对照规则检查： 1. 仅输出 HTML 片段？是的。 2. 每个自然段落用 `
` 包裹，长文本使用 `
`/`
`？是的。 3. 列表
`，代码使用 ``，重要句子使用 ``。 4. 保留原文所有信息，无增删改。 5. 不输出 ``/``/`` 标签。 2. 分析输入文本： * *第一段：* Cyber-physical smart-grid detectors are typically treated as black boxes and validated exclusively on clean, temporally shuffled datasets, which risk overly optimistic performance claims. This work presents a multi-testbed evaluation study built around an interpretable, time-windowed fusion Random Forest, adopting the pessimistic result whenever evaluation protocols disagree. * *项目列表（核心发现）：* * (i) On a public paired cyber-physical dataset, shuffled cross-validation is optimistic: macro-F1 falls from 0.96 to 0.88 +/- 0.06 under rolling-origin temporal evaluation. A topology-free graph convolutional network (GCN) serving as a control confirms that this lightweight model is competitive on these splits. * (ii) The adaptive and adaptive-joint feature-space attacks, which are infeasible as realistic grid manipulations, degrade macro-F1 to 0.67 and 0.34, respectively. Adversarial training and randomized smoothing partially restore performance, and a physics-constrained evolution-strategy evasion on the IEEE 14-Bus testbed shows the same vulnerability pattern. * (iii) On real IEC-104 traffic, the cyber view reliably catches structural attacks (recall of 0.87 to 0.98) but largely fails on semantic value manipulation (recall of 0.11 to 0.35). An observability taxonomy on the IEEE 14-Bus system reveals that stealthy false data injection defeats all three observation views. * (iv) On a full AC weighted least-squares state-estimation testbed, a forecasting-aided temporal test is evaded by slow ramps precisely where a rate-invariant physics-manifold test sustains detection at a common 5% false-alarm target. A full end-to-end evaluation on IEEE 118-Bus confirms the ordering but bounds its strength: the manifold test detects every trial down to 0.05 degree/step and 0.48 at 0.02 degree/step, against 0.14 for the temporal test. All code and benchmarks are released. 3. 规划 HTML 结构： * 输入文本有一个开头段落，然后是四个要点 (i, ii, iii, iv)。我可以将这四个要点分组在一个简短的 `` 下，形成一个结构良好的专业报告（根据规则 2：“长文用
/
划分小节”）。 * 让我们添加一个 `
` 作为主要章节标题，例如 “研究概述与核心发现” 或者契合新闻主题的标题。参考文本中的措辞，最准确的可能是 “研究背景与核心发现”，因为它是工作内容里的明确短语。 * 我将使用 `
` “实验结果与评估” 作为四个要点列表的标题，该标题与文本非常契合。 * 列表中的所有文本都需要严格保留。我会将列表项包裹在 `
` 中。 * 我应该为第一句或核心结论添加 ``。规则说“重要句子可用”。 * 第一段：“Cyber-physical smart-grid detectors are typically treated as black boxes and validated exclusively on clean, temporally shuffled datasets, which risk overly optimistic performance claims.” 推断为重要句子，用 `` 包裹。 * 第 (i) 点：“shuffled cross-validation is optimistic” 用 `` 包裹。 * 第 (ii) 点：“degrade macro-F1 to 0.67 and 0.34” 用 `` 包裹。 * 第 (iii) 点：“the cyber view reliably catches structural attacks (recall of 0.87 to 0.98) but largely fails on semantic value manipulation (recall of 0.11 to 0.35)” 用 `` 包裹。 * 第 (iv) 点：“the manifold test detects every trial down to 0.05 degree/step and 0.48 at 0.02 degree/step, against 0.14 for the temporal test” 用 `` 包裹。 * 检查特殊字符：`0.88 +/- 0.06`，`IEEE 14-Bus`，`IEC-104`，`0.05 degree/step`。没有 CSS 转义的字符。文本极其干净。 * 确保不添加任何Markdown代码块（```html …… ```）或外部评论。 4. 构建 HTML： ```html
研究背景与核心发现
Cyber-physical smart-grid detectors are typically treated as black boxes and validated exclusively on clean, temporally shuffled datasets, which risk overly optimistic performance claims. This work presents a multi-testbed evaluation study built around an interpretable, time-windowed fusion Random Forest, adopting the pessimistic result whenever evaluation protocols disagree.
实验结果与评估
``` 5. 对照规则检查： 1. 仅输出 HTML 片段？是的。 2. 每个自然段落用 `
- (i) On a public paired cyber-physical dataset, shuffled cross-validation is optimistic: macro-F1 falls from 0.96 to 0.88 +/- 0.06 under rolling-origin temporal evaluation. A topology-free graph convolutional network (GCN) serving as a control confirms that this lightweight model is competitive on these splits.
- (ii) The adaptive and adaptive-joint feature-space attacks, which are infeasible as realistic grid manipulations, degrade macro-F1 to 0.67 and 0.34, respectively. Adversarial training and randomized smoothing partially restore performance, and a physics-constrained evolution-strategy evasion on the IEEE 14-Bus testbed shows the same vulnerability pattern.
- (iii) On real IEC-104 traffic, the cyber view reliably catches structural attacks (recall of 0.87 to 0.98) but largely fails on semantic value manipulation (recall of 0.11 to 0.35). An observability taxonomy on the IEEE 14-Bus system reveals that stealthy false data injection defeats all three observation views.
- (iv) On a full AC weighted least-squares state-estimation testbed, a forecasting-aided temporal test is evaded by slow ramps precisely where a rate-invariant physics-manifold test sustains detection at a common 5% false-alarm target. A full end-to-end evaluation on IEEE 118-Bus confirms the ordering but bounds its strength: the manifold test detects every trial down to 0.05 degree/step and 0.48 at 0.02 degree/step, against 0.14 for the temporal test. All code and benchmarks are released.
` 包裹，长文本使用 `
`/`
`？是的。 3. 列表