In this paper, we present a reinforcement learning (RL) method for solving
optimal false data injection attack problems in probabilistic Boolean control
networks (PBCNs) where the attacker lacks knowledge of the system model.
Specifically, we employ a Q-learning (QL) algorithm to address this problem. We
then propose an improved QL algorithm that not only enhances learning
efficiency but also obtains optimal attack strategies for large-scale PBCNs
that the standard QL algorithm cannot handle. Finally, we verify the
effectiveness of our proposed approach by considering two attacked PBCNs,
including a 10-node network and a 28-node network.

通过采用强化学习方法（特别是 Q-learning 算法），解决了在概率布尔控制网络中，攻击者模型不可知情况下的最优虚假数据注入问题，并提出了一种改进的 QL 算法，能够处理标准 QL 算法无法处理的大规模 PBCNs 并获取最优攻击策略，并通过两个被攻击的 PBCN 网络验证了我们提出方法的有效性。

基于 Q 学习的概率布尔控制网络的最优虚假数据注入攻击

Q-learning Based Optimal False Data Injection Attack on Probabilistic  Boolean Control Networks

False Data Injection Attack (FDIA) has become a growing concern in modern
cyber-physical power systems. Most existing FDIA detection techniques project
the raw measurement data into a high-dimensional latent space to separate
normal and attacked samples. These approaches focus more on the statistical
correlations of data values and are therefore susceptible to data distribution
drifts induced by changes in system operating points or changes in FDIA types
and strengths, especially for FDIA localization tasks. Causal inference, on the
other hand, extracts the causality behind the coordinated fluctuations of
different measurements. The causality patterns are determined by fundamental
physical laws such as Ohm's Law and Kirchhoff's Law. They are sensitive to the
violation of physical laws caused by FDIA, but tend to remain stable with the
drift of system operating points. Leveraging this advantage, this paper
proposes a joint FDIA detection and localization framework based on causal
inference and the Graph Attention Network (GAT) to identify the attacked system
nodes. The proposed framework consists of two levels. The lower level uses the
X-learner algorithm to estimate the causality strength between measurements and
generate Measurement Causality Graphs (MCGs). The upper level then applies a
GAT to identify the anomaly patterns in the MCGs. Since the extracted causality
patterns are intrinsically related to the measurements, it is easier for the
upper level to figure out the attacked nodes than the existing FDIA
localization approaches. The performance of the proposed framework is evaluated
on the IEEE 39-bus system. Experimental results show that the causality-based
FDIA detection and localization mechanism is highly interpretable and robust.

基于因果推断和图注意力网络（GAT）的联合 FDIA 检测和定位框架用于识别受攻击的系统节点，通过提取因果关系模式，该框架在 FDIA 检测和定位任务上表现出高解释性和稳健性。

从测量中提取物理因果关系以侦测和定位假数据注入攻击

Extracting Physical Causality from Measurements to Detect and Localize  False Data Injection Attacks

Data analysis and monitoring on smart grids are jeopardized by attacks on
cyber-physical systems. False data injection attack (FDIA) is one of the
classes of those attacks that target the smart measurement devices by injecting
malicious data. The employment of machine learning techniques in the detection
and localization of FDIA is proven to provide effective results. Training of
such models requires centralized processing of sensitive user data that may not
be plausible in a practical scenario. By employing federated learning for the
detection of FDIA attacks, it is possible to train a model for the detection
and localization of the attacks while preserving the privacy of sensitive user
data. However, federated learning introduces new problems such as the
personalization of the detectors in each node. In this paper, we propose a
federated learning-based scheme combined with a hybrid deep neural network
architecture that exploits the local correlations between the connected power
buses by employing graph neural networks as well as the temporal patterns in
the data by using LSTM layers. The proposed mechanism offers flexible and
efficient training of an FDIA detector in a distributed setup while preserving
the privacy of the clients. We validate the proposed architecture by extensive
simulations on the IEEE 57, 118, and 300 bus systems and real electricity load
data.

本文提出了一种联邦学习机制，结合混合卷积神经网络体系结构，并利用图形神经网络和 LSTM 层中的本地相关性和数据时间模式，以在分布式设置中灵活且高效地训练 FDIA 检测器，同时保护客户的隐私。