Year: 2025
Type: article
Abstract: 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.
Source: Applied Sciences
DOI: 10.3390/app15115804
Authors: Shifeng Jia, Yating Zhao, Yang Zhang, Bin Jia, Wenjuan Lian
Institution: Shandong University of Science and Technology
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G3548508515, G5449657557
OpenAlex ID: W4410598579