[论文解读] False Data Injection Attacks in Internet of Things and Deep Learning enabled Predictive Analytics

Gautam Raj Mode, Prasad Calyam+1 more|arXiv (Cornell University)|Oct 3, 2019

Adversarial Robustness in Machine Learning参考文献 33被引用 16

一句话总结

本文研究了在物联网（IoT）驱动的工业环境中，虚假数据注入攻击（FDIA）对基于深度学习的预测性维护（PdM）系统的影响，采用C-MAPSS涡轮风扇发动机数据集进行分析。结果表明，即使在传感器层面存在轻微的FDIA，也会显著降低LSTM、GRU和CNN模型在剩余使用寿命（RUL）预测中的准确性，其中GRU模型表现出更强的鲁棒性，同时识别出序列长度、准确性与攻击鲁棒性之间存在关键权衡。

ABSTRACT

Industry 4.0 is the latest industrial revolution primarily merging automation with advanced manufacturing to reduce direct human effort and resources. Predictive maintenance (PdM) is an industry 4.0 solution, which facilitates predicting faults in a component or a system powered by state-of-the-art machine learning (ML) algorithms and the Internet-of-Things (IoT) sensors. However, IoT sensors and deep learning (DL) algorithms, both are known for their vulnerabilities to cyber-attacks. In the context of PdM systems, such attacks can have catastrophic consequences as they are hard to detect due to the nature of the attack. To date, the majority of the published literature focuses on the accuracy of DL enabled PdM systems and often ignores the effect of such attacks. In this paper, we demonstrate the effect of IoT sensor attacks on a PdM system. At first, we use three state-of-the-art DL algorithms, specifically, Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Convolutional Neural Network (CNN) for predicting the Remaining Useful Life (RUL) of a turbofan engine using NASA's C-MAPSS dataset. The obtained results show that the GRU-based PdM model outperforms some of the recent literature on RUL prediction using the C-MAPSS dataset. Afterward, we model two different types of false data injection attacks (FDIA) on turbofan engine sensor data and evaluate their impact on CNN, LSTM, and GRU-based PdM systems. The obtained results demonstrate that FDI attacks on even a few IoT sensors can strongly defect the RUL prediction. However, the GRU-based PdM model performs better in terms of accuracy and resiliency. Lastly, we perform a study on the GRU-based PdM model using four different GRU networks with different sequence lengths. Our experiments reveal an interesting relationship between the accuracy, resiliency and sequence length for the GRU-based PdM models.

研究动机与目标

• 分析基于深度学习的预测性维护（PdM）系统在物联网驱动的工业系统中对虚假数据注入攻击（FDIA）的脆弱性。
• 评估LSTM、GRU和CNN模型在NASA C-MAPSS数据集上预测剩余使用寿命（RUL）的性能。
• 对传感器数据建模并应用两种类型的FDIA——持续型与间歇型，并评估其对RUL预测准确性的影响。
• 研究GRU-based PdM模型中序列长度、模型准确性与FDIA鲁棒性之间的关系。
• 通过平衡准确性与安全性，为设计更具鲁棒性与抗攻击能力的PdM系统提供实证指导。

提出的方法

• 在C-MAPSS数据集上训练了三种最先进的深度学习模型——LSTM、GRU和CNN，以预测涡轮风扇发动机的剩余使用寿命（RUL）。
• 对两种类型的虚假数据注入攻击（FDIA）进行了建模：持续型（持续性操纵）与间歇型（周期性操纵）传感器数据。
• 将攻击应用于少量传感器，通过将预测结果与真实值对比，衡量性能下降程度。
• 训练并评估了四种不同序列长度（10、30、50、100）的GRU网络，以研究序列长度对准确性与鲁棒性的影响。
• 使用RMSE（均方根误差）量化性能，采用分段式RUL预测以可视化攻击随时间的影响。
• 采用对比评估框架，评估模型在随机和有偏FDIA场景下的鲁棒性。

实验结果

研究问题

• RQ1 持续型与间歇型虚假数据注入攻击如何影响基于LSTM、GRU和CNN的PdM系统中RUL预测的准确性？
• RQ2 在PdM应用中，LSTM、GRU或CNN哪种深度学习架构对FDIA表现出最高的鲁棒性？
• RQ3 序列长度与GRU-based PdM模型在准确性与鲁棒性综合表现之间存在何种关系？
• RQ4 对少数传感器实施FDIA在多大程度上会损害基于深度学习的预测性维护系统的可靠性？
• RQ5 通过序列长度变化获得的实证洞察，能否指导在对抗性条件下设计更鲁棒且更准确的PdM系统？

主要发现

• 基于GRU的PdM模型在C-MAPSS数据集上的表现优于近期文献报道，其RMSE低于LSTM与CNN模型。
• 持续型FDIA使CNN模型的RMSE最高增加至真实RMSE的6倍，GRU模型最高增加至真实RMSE的8倍。
• 间歇型FDIA使CNN模型的RMSE最高增加至真实RMSE的18倍，GRU模型最高增加至真实RMSE的4倍。
• 基于GRU的模型在随机与有偏FDIA下均表现出更强的鲁棒性，优于LSTM与CNN模型的抗攻击能力。
• GRU3（序列长度50）在准确性与鲁棒性之间实现了最佳平衡，尽管其准确性略低于GRU2（序列长度30）。
• 观察到序列长度、模型准确性与FDIA鲁棒性之间存在显著权衡，表明序列长度是构建安全PdM系统的关键设计参数。