## 4 Defense Strategies Against Deliberate Attacks on IECPS
### Process Response
#### 4.2.1 Deliberate Attack Detection
In literature [126], an attacker-defender Bayesian game-theoretic detection model for FDIA attacks is developed to determine protection choices for critical measurements. In [127], a Bayesian model-based anomalous data traffic detection mechanism is presented, which distinguishes between DoS attacks in wireless sensor networks and high traffic generated by legitimate user communications. QCD is a method for rapid detection of sudden changes by minimizing the delay between the occurrence of the change and its monitoring through sequential or real-time observations. In [128], multiple meters are used to measure different output signals of a PV system, and the time correlation of fault signals and the signal correlation between different  
Figure 13: Detection flow based on dynamic state estimation. meters are utilized to achieve fast fault detection. A PMU phase angle measurement acquisition method based on line interruption transient dynamics algorithm was developed in [129], which is able to quickly acquire real-time PMU measurements and speed up the identification of line interruptions. Since QCD is a dynamic detection framework, it is often used together with cumulative sum test (CUSUM), dynamic state estimation, and Bayesian modeling. In [130], the use of the CUSUM algorithm is proposed for detecting spurious data injected into energy management systems. In [131], the adaptive level-triggered sampling technique is applied on this basis to achieve more accurate detection; however, the method is unable to detect covert FDIA attacks. In [132], a dynamic estimation algorithm is proposed to estimate and track time-varying and non-stationary grid states, and a QCD algorithm for distinguishing between FDIA and sudden system changes is developed by analyzing the statistical properties of dynamic state estimation. This involves identifying the parameters of the detector and quantifying the performance of the detection model based on the Markov chain model. * **Data-driven detection methods** With the development of AI, various machine learning methods have been widely applied to detect deliberate attacks, both offline and online [133]. Among them, supervised learning classifiers are the most popular attack detection technique, which utilizes historical data to train a model to reflect the operational state and statistical characteristics of the energy system [110]. With sufficient historical data training, the method produces better models, leading to more accurate detection results. In [134], three algorithms, K-nearest neighbor, support vector machine, and sparse logistic regression, are used for attack classification. A supervised learning attack detection framework is established to address the issue of sparse measurement matrices in the state estimation method. In [135], a two-layer neural network architecture based on sequence decomposition reconstruction is proposed, combining decision trees with long and short-term memory neural networks. This architecture can accurately identify and remove attack data from PMU data. In [136], deep learning techniques are used to learn the FDIA attack behavior patterns in historical measurement data. The difference in patterns between identifying damaged data subjected to FDIA and normal data serves as the basis for classification. However, this method cannot cope with scenarios in which the topology undergoes drastic changes over a short period. In [137], more protocol features are extracted by wavelet decomposition of protocol messages. These extracted features are then learned by deep neural networks, which mines the attack behaviors from two aspects: temporal features and spatial features. The recognition ability of highly stealthy false data injection attacks is superior to that of traditional deep learning methods. Although the aforementioned methods exhibit high detection accuracy, they necessitate a large number of training datasets, which are often challenging to acquire, particularly complete attack data in practical operations.