# CAA-RF: An Anomaly Detection Algorithm for Computing Power Blockchain Networks
## 3 Network Anomaly Detection Strategy for Computing Power Blockchain Networks  
The anomaly detection architecture centers on CPBN anomaly detection. By constructing the CPBN anomaly detection system and designing the CAA-RF anomaly detection algorithm, a six-dimensional integrated security system is established in shown Figure 2. Driven by both information security and network security, the system concretizes three core mechanisms--governance, response, and recovery--through a dedicated security capability middleware. A federated dynamic identity authentication mechanism constructs a zero-trust access system, while fully homomorphic encryption provides mathematically rigorous protection for the secure data exchange service center. A specially designed quantum-resilient transitional system serves as a futuristic shield, reserving upgrade potential for defense against post-quantum era threats. At the top level, a risk correlation hub enables visualization of threat landscapes, forming standardized, externally exportable security assurance capability packages. The foundational security capability layer adopts a cellular defense layout, deploying differentiated technological arsenals across six major domains, including terminals, applications, and systems. On the terminal side, immunity barriers are established through node protection and automated penetration testing. At the network boundary, firewalls and traffic scrubbing mechanisms form dynamic defensive perimeters. Notably, the data security protection framework exhibits a three-dimensional depth: vulnerability scanning systems act as all-weather sentinels, transparent data desensitization technologies ensure data usability without visibility, and log auditing systems construct a comprehensive operational gene library. This layer extends downward into a data management hub, enforcing full lifecycle control over six categories of data flows, including metadata and device data, thus creating a multi-dimensional protection net that spans from physical resource pools to third-party components. As the tactical execution center, the CPBN anomaly detection system leverages blockchain technology to construct a real-time cyber defense sandbox. The intranet monitoring module anticipates lateral movement risks through traffic fingerprint analysis, while the extranet monitoring unit employs machine learning to model DDoS attack patterns. Upon detection of a Sybil attack, the system rapidly identifies anomalous nodes via consensus algorithms and synchronously activates a collaborative mitigation channel for on-chain isolation. The specially designed threat hunting system transforms security incidents into traceable blockchain data and interacts with the risk correlation hub via smart contracts, thereby realizing a closed-loop defense mechanism from detection to recovery. Figure 2: Network Anomaly Detection Architecture for Blockchain Computing Power Networks. This architecture achieves a spiral-evolving security ecosystem through a three-tier synergy of strategic planning, capability support, and execution feedback. Blue data streams between modules function as a neural network transmitting threat intelligence, while white functional blocks clearly delineate responsibilities. Whether handling a DDoS attack through a comprehensive chain from traffic scrubbing to source tracing or responding to a Sybil attack via coordinated identity authentication and node protection, the system embodies the design philosophy of defense-in-depth and intelligent linkage. It provides a predictive and adaptive security foundation for blockchain-based networks. Unlike most existing studies that adopt parallel ensemble strategies or single deep learning models, this paper proposes a novel framework that serially integrates traditional machine learning methods with deep learning models.