## 4 Defense Strategies Against Deliberate Attacks on IECPS
### Process Response
#### 4.2.1 Deliberate Attack Detection
In [138], to address the issue of imbalance among normal, faulty, and attack datasets, an extreme gradient-enhanced classifier based on integrated learning is employed to improve dataset balance. Beyond overcoming the imbalance of training samples in AI-based detection of deliberate attacks, considerations regarding real-time detection and attack localization are also essential [139]. In [140], a Bayesian-based approximation filter is utilized to minimize communication overhead and time complexity, thereby enhancing both real-time performance and immunity against cyberattack detection in surveillance systems. In [141], a detection method based on attention-deep reinforcement learning is proposed for intrusion identification defense, with deliberate data attacks localized using an enhanced graph convolutional network algorithm. Considering the high dependence of supervised learning on prior knowledge, the researchers tried to introduce semi-supervised learning into system attack detection. Figure 14 illustrates the distinction between supervised and semi-supervised learning detection algorithms, highlighting that the training set for semi-supervised learning detection algorithms comprises both labeled and unlabeled datasets. A semi-supervised anomaly detection model was built in [142] and the results proved that semi-supervised methods have better detection performance. In [143], semi-supervised K-means clustering is utilized to learn different attack patterns and achieve the recognition of multiple attack types. However, the method relies heavily on the label quality of the original training samples. To solve this problem, in [144], a semi-supervised deep learning method is used to train a small number of labeled samples and a large number of unlabeled samples to achieve accurate detection of deliberate attacks. Although semi-supervised learning can achieve attack detection with fewer samples of attack data, it is still not free from the limitation of labels. The obtained model has limited generalization ability and may struggle to adapt to the dynamic changes in the system. Unsupervised learning is able to learn the relationship between the operational data autonomously and can remove the labeling limitation. In [145], system parameters and topology are used as validation information, and a fuzzy C-mean clustering algorithm is used to realize unsupervised detection of attack messages. This method maintains high accuracy under topology changes. In [146], an adversarial migration learning framework is used to establish the mapping relationship between system parameters and messages, and unsupervised learning of attacks is realized by referring to system parameters and topology data. Although the above studies performed unsupervised learning of attack features, they need to utilize system parameters and topology data for assistance. In [147], using local outlier factor analysis technique, an unsupervised detection method without the need for system parameters and topology data is proposed to achieve accurate detection of FDIA.