# CAA-RF: An Anomaly Detection Algorithm for Computing Power Blockchain Networks
## 1 Introduction
### Related Work
This integration of blockchain features with detection technologies provides a reference for this thesis to optimize anomaly detection methods by incorporating underlying blockchain mechanisms [19]. Additionally, regarding the intelligence of detection models, in 2023, [PERSON] et al. proposed a deep learning model based on autoencoders and an improved AlexNet, achieving efficient real-time detection of network intrusions. Although their application scenario was traditional network security, the deep learning architecture offers insights for improving the accuracy and real-time performance of blockchain anomaly detection [20]. In the field of ensemble learning, in 2021, [PERSON] et al. proposed a network intrusion detection system combined with a parallel stacking ensemble method, which obtained the highest accuracy of 80.10% on the NSL-KDD dataset, effectively improving the detection performance [21]. In 2022, [PERSON] et al. systematically compared the effectiveness of various machine learning methods, such as SVM, random forest, and XGBoost in blockchain anomaly detection, pointing out that ensemble learning algorithms perform excellently in large-scale data analysis [22]. These achievements provide a theoretical foundation for model selection and ensemble optimization in this thesis. In 2023, [PERSON] et al. proposed a supervised anomaly detection method based on conditional Generative Adversarial Networks and ensemble active learning [23]. By employing a parallel architecture of multiple discriminators and an active sampling strategy, detection performance was effectively improved. This method offers valuable insights for addressing data challenges in blockchain anomaly detection in this thesis. Moreover, LSTM, Variational Autoencoders, deep belief networks, Generative Adversarial Networks, and Recurrent Neural Networks have also been applied to anomaly detection in recent years. In 2021, [PERSON] et al. proposed an industrial process anomaly detection method based on Recurrent Neural Networks (RNNs) [24]. By predicting the states of industrial processes and comparing them with actual values, this method achieved efficient intrusion and anomaly detection without requiring training on anomalous samples. In 2023, [PERSON] et al. proposed a transformer oil temperature anomaly detection method based on Long Short-Term Memory (LSTM) Networks and attention mechanisms [25]. This method effectively learns long-term dependencies in time series and improves prediction accuracy through the attention mechanism, enabling efficient detection of oil temperature anomalies. In 2022, [PERSON] et al. proposed an e-commerce data anomaly detection method based on Variational Autoencoders (VAEs) [26]. By learning the latent distribution of data, this method effectively extracts features and identifies anomalous data, outperforming traditional detection methods in terms of accuracy and robustness. In 2021, [PERSON] et al. proposed a power system state estimation and anomaly detection method based on deep belief networks (DBNs) [27]. Leveraging the powerful feature extraction ability of DBNs, this method achieved high-precision fault detection and recovery time prediction in power systems. In 2022, [PERSON] et al. proposed a time series anomaly detection method based on Generative Adversarial Networks (GANs) [28]. By generating synthetic data and capturing complex patterns with GANs, this method significantly improved the accuracy and robustness of time series anomaly detection. Although these Variational Autoencoders and Generative Adversarial Networks were originally mainly used for generative tasks such as image generation and data augmentation, many studies have shown that, with appropriate adjustments and design, they can also be applied to classification tasks, especially in anomaly detection and classification problems in specific domains.