网络与信息安全学报 ›› 2024, Vol. 10 ›› Issue (4): 17-36.doi: 10.11959/j.issn.2096-109x.2024050
吴越, 曹国彦
收稿日期:
2024-01-19
修回日期:
2024-07-28
出版日期:
2024-08-25
发布日期:
2024-09-14
作者简介:
基金资助:
Yue WU, Guoyan CAO
Received:
2024-01-19
Revised:
2024-07-28
Online:
2024-08-25
Published:
2024-09-14
Supported by:
摘要:
随着工业控制系统与信息网络的深度融合,工业关键基础设备的网络化、智能化成为未来工业发展的趋势。工业传感网络作为工业系统网络化的重要组成部分,其数据安全已成为被工业安全领域广泛关注。工业传感网络数据异常影响工业控制系统的物理安全、信息安全和网络安全。工业传感网络异常检测是面向网络攻击及物理故障,通过对复杂、多层次、多尺度的传感时间序列分析,发现隐蔽的异常逻辑及故障原因的方法。总结了工业传感网络异常的成因,系统地综述了工业传感网络异常检测的研究进展,从时序特征、时空多尺度及非结构图表征3个视角,对工业传感网络异常检测的关键技术及典型方法进行分类阐述,分析现有各类方法的发展脉络及主要突破。介绍了用于工业传感网络的数据集和评价指标,及方法的检测效果,并通过对比这些方法的实验结果,说明了各方法的特点及技术侧重,给出了现有工作的应用前景,梳理出当前异常检测方法在实际应用中所面临的挑战。最后提出了工业传感网络异常检测发展趋势及未来的研究方向。
中图分类号:
吴越, 曹国彦. 面向工业传感网络的时间序列异常检测综述[J]. 网络与信息安全学报, 2024, 10(4): 17-36.
Yue WU, Guoyan CAO. Survey of time series anomaly detection for industrial sensor networks[J]. Chinese Journal of Network and Information Security, 2024, 10(4): 17-36.
表1
造成异常的网络攻击模型"
相关工作 | 异常类型 | 应用领域 | 简要描述 | 涉及安全属性 | ||
---|---|---|---|---|---|---|
可用性 | 完整性 | 机密性 | ||||
Muraleedharan et al[ | DoS | 智能读卡系统 | 传感器通过蠕虫洞进行伪装 | ● | ○ | ○ |
Peng et al[ | DoS | 网络物理系统 | 干扰传感器和远程间的通信 | ● | ○ | ○ |
Zhang et al[ | DoS | 无线网络系统 | 最大化二次高斯代价函数 | ● | ○ | ○ |
Qin et al[ | DoS | 网络物理系统 | 研究未攻击下的数据丢包 | ● | ○ | ○ |
Zhang et al[ | DoS | 网络物理系统 | 多传感器共享公共通信信道 | ● | ○ | ○ |
Wu et al[ | FDI | 网络物理系统 | 使用时变权重矩阵增加难度 | ○ | ● | ○ |
Mousavinejad et al[ | FDI | 网络化控制系统 | 用传感器测量数据更新预测 | ○ | ● | ○ |
Wu et al[ | FDI | 网络物理系统 | 在智能动态传感中降低质量 | ○ | ● | ○ |
Zhang et al[ | FDI | 无人地面飞行器 | 用Kullback-Leibler约束 | ○ | ● | ○ |
Li et al[ | FDI | 智能电网 | 保守和侵略两种拓扑攻击 | ○ | ● | ○ |
Wang et al[ | 被动 | 水声传感网络 | 建立能量和位置的消耗模型 | ○ | ○ | ● |
Yuan et al[ | 被动 | 网络物理系统 | 待反馈随机算法最大化传输 | ○ | ○ | ● |
表2
异常检测方法在精确率上的实验结果"
方法 | 精确率 | ||||
---|---|---|---|---|---|
SWaT | WADI | SMAP | MSL | SMD | |
DAGMM | 27.46% | 87.79% | 58.45% | 54.12% | 91.03% |
LSTM-VAE | 96.24% | 87.79% | 85.51% | 52.57% | — |
USAD | 99.77% | 18.73% | 74.80% | 79.49% | 90.60% |
OmniAnomaly | 97.82% | 31.58% | 81.30% | 78.48% | 88.81% |
LSTM-NDT | 77.78% | 1.38 | 85.23% | 62.88% | 97.36% |
Anomaly Transformer | 72.51% | — | 91.85% | 79.61% | 88.91% |
TranAD | 97.60% | 35.29% | 80.43% | 90.38% | 92.62% |
MSCRED | 99.92% | 25.13% | 81.75% | 89.12% | 72.76% |
MAD-GAN | 98.97% | 41.44% | 80.49% | 85.17% | 99.91% |
TimesNet | 88.31% | — | 92.52% | 83.92% | 88.66% |
MTAD-GAT | 97.18% | 28.18% | 89.06% | 87.54% | 82.10% |
GDN | 99.35% | 97.50% | 74.80% | 93.08% | 71.70% |
GTA | 88.10% | 83.61% | 91.76% | 91.17% | — |
表3
异常检测方法在召回率上的实验结果"
方法 | 召回率 | ||||
---|---|---|---|---|---|
SWaT | WADI | SMAP | MSL | SMD | |
DAGMM | 69.52% | 26.99% | 90.58% | 99.34% | 99.14% |
LSTM-VAE | 59.91% | 14.45% | 63.66% | 95.46% | — |
USAD | 68.79% | 82.96% | 96.27% | 99.12% | 99.74% |
OmniAnomaly | 69.57% | 65.41% | 94.19% | 99.24% | 99.85% |
LSTM-NDT | 51.09% | 78.23% | 73.26% | 100.0% | 84.40% |
Anomaly Transformer | 97.32% | — | 58.11% | 87.37% | 82.23% |
TranAD | 69.97% | 82.96% | 99.99% | 99.99% | 99.74% |
MSCRED | 67.70% | 73.19% | 92.16% | 98.62% | 99.74% |
MAD-GAN | 63.74% | 33.92% | 82.14% | 89.91% | 84.40% |
TimesNet | 96.24% | — | 58.29% | 86.42% | 83.14% |
MTAD-GAT | 69.57% | 80.12% | 91.23% | 94.40% | 92.15% |
GDN | 68.12% | 40.19% | 98.91% | 98.92% | 99.74% |
GTA | 88.10% | 83.61% | 91.76% | 91.17% | — |
表4
异常检测方法在F1分数上的实验结果"
方法 | F1分数 | ||||
---|---|---|---|---|---|
SWaT | WADI | SMAP | MSL | SMD | |
DAGMM | 0.39 | 0.36 | 0.71 | 0.67 | 0.95 |
LSTM-VAE | 0.74 | 0.25 | 0.73 | 0.68 | 0.83 |
USAD | 0.81 | 0.31 | 0.84 | 0.88 | 0.95 |
OmniAnomaly | 0.82 | 0.42 | 0.87 | 0.88 | 0.95 |
LSTM-NDT | 0.62 | 0.03 | 0.79 | 0.77 | 0.90 |
Anomaly Transformer | 0.83 | — | 0.71 | 0.83 | 0.85 |
TranAD | 0.82 | 0.50 | 0.89 | 0.95 | 0.96 |
MSCRED | 0.81 | 0.37 | 0.87 | 0.94 | 0.84 |
MAD-GAN | 0.77 | 0.37 | 0.81 | 0.87 | 0.91 |
TimesNet | 0.92 | — | 0.72 | 0.85 | 0.86 |
MTAD-GAT | 0.81 | 0.42 | 0.90 | 0.91 | 0.87 |
GDN | 0.81 | 0.57 | 0.85 | 0.96 | 0.83 |
GTA | 0.91 | 0.84 | 0.90 | 0.91 | — |
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