CAA-RF: An Anomaly Detection Algorithm for Computing Power Blockchain Networks - Shifeng Jia; Yating Zhao; Yang Zhang; Bin Jia; Wenjuan Lian (Applied Sciences)
As a distributed communication and storage system, blockchain forms a Computing Power Blockchain Network (CPBN) by integrating computing nodes and network resources. However, its open architecture faces major security threats such as Sybil attacks, computational fraud, and DDoS attacks. Traditional detection methods often fail in dynamic environments with scarce domain data. To address this, we developed a lightweight blockchain simulator to generate Sybil and DDoS attack scenarios, constructing a 14-dimensional feature dataset. To address Sybil attacks and distributed denial-of-service attack scenarios, this paper proposes an adaptive attention random forest convolutional neural network anomaly detection method (CAA-RF). Our approach uses multi-layer convolutional operations to capture high-order data correlations, combines attention mechanisms for global dependency modeling, and employs random forest for robust anomaly detection, enabling effective real-time security protection for blockchain systems.