CAA-RF: An Anomaly Detection Algorithm for Computing Power Blockchain Networks
Source abstract
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: crossref · Quality: baseline
Research context
OpenAlex topics: Network Security and Intrusion Detection · Smart Grid Security and Resilience · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
College of Computer Science and Engineering, Shandong University of Science and Technology, No. 579 Qianwan Port Road, Huangdao District, Qingdao 266590, China
Funding
Postdoctoral Fellowship Program of CPSF · China Postdoctoral Science Foundation · Shandong Postdoctoral Science Foundation · Qingdao Postdoctoral Project
References (36)
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