# CAA-RF: An Anomaly Detection Algorithm for Computing Power Blockchain Networks
## 1 Introduction
### Related Work
Finally, the model's performance in handling complex and long-term threats still requires further observation and improvement. [PERSON]'s CL-IDS model combined convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) Networks, performing outstandingly on the KDDCUP99 dataset [8]. In 2023, [PERSON], [PERSON], [PERSON], and [PERSON] proposed a network traffic anomaly detection method capable of tolerating concept drift [9]. By integrating multiple deep learning models to effectively address the issue of concept drift in data streams, the method enhances the ability to detect anomalous traffic. Each sub-classifier offers a unique perspective to learn data features, and through this approach, the solution can accurately capture changes in data concepts and apply weight layers to help the model adapt to changes caused by concept drift. [PERSON]'s approach combined autoencoders and Mahalanobis distance, using unsupervised learning and ensemble methods to improve anomaly detection accuracy [10]. Although deep learning models can capture complex patterns, they often require large amounts of training data and are prone to overfitting, especially in cases of data imbalance. Additionally, deep models tend to have poor interpretability and robustness. In addition to purely using deep models, innovation on existing models and the integration of multiple models is also a new approach. In 2023, [PERSON] proposed methods that combine deep learning, clustering algorithms, time series analysis, and other techniques, aiming to improve the overall performance of detection systems [11]. [PERSON] proposed an unsupervised deep learning strategy combining Generative Adversarial Networks (GANs) and autoencoders, though it performed poorly in handling minority and unknown attacks [12]. [PERSON] introduced a novel machine learning-based method for detecting and classifying botnets, primarily using deep learning techniques, particularly the ResNet residual network, and converting network traffic into grayscale images for recognition [13]. In 2022, [PERSON] and others proposed a new semi-supervised learning method based on autoencoders (AE), achieving significant progress in improving detection efficiency and reducing computational costs [14]. In 2021, [PERSON] proposed a multi-type anomaly detection method based on raw network traffic, and the visualization approach can directly identify anomalies from raw data, though it faces risks of data imbalance and overfitting [15]. In 2024, [PERSON] proposed a deep learning-based DDoS attack detection solution, relying on a two-layer GRU gated recurrent unit model to detect DDoS attacks, combining an encoder/decoder architecture to reduce data dimensionality and recover data through the decoding layer [16]. In 2021, [PERSON] introduced the entropy rate of change as a new metric, enhancing the sensitivity of entropy-based anomaly detection methods, especially in detecting covert attacks [17]. While innovative and hybrid methods aim to overcome the limitations of single technologies, they may introduce additional challenges due to increased complexity, such as reduced model interpretability and potential resource consumption issues in practical deployment. In recent years, significant progress has been made in blockchain anomaly detection research worldwide, with related methods continually being enriched and refined. In 2022, [PERSON] et al. proposed an unsupervised anomaly detection method that identifies price anomalies in blockchain NFT transactions through clustering and statistical analysis, effectively uncovering potential money laundering and market manipulation behaviors [18]. In terms of enhancing security, in 2021, [PERSON] et al. combined blockchain technology with traditional network intrusion detection systems (IDS), leveraging the immutability of blockchain data to enhance the security and traceability of detection systems.