Lightweight and Identifier-Oblivious Engine for Cryptocurrency Networking Anomaly Detection

【Author】 Fan, Wenjun; Hong, Hsiang-Jen; Kim, Jinoh; Wuthier, Simeon; Nakashima, Makiya; Zhou, Xiaobo; Chow, Ching-Hua; Chang, Sang-Yoon

【Source】IEEE TRANSACTIONS ON DEPENDABLE AND SECURE COMPUTING

【影响因子】6.791

【Abstract】The distributed cryptocurrency networking is critical because the information delivered through it drives the mining consensus protocol and the rest of the operations. However, the cryptocurrency peer-to-peer (P2P) network remains vulnerable, and the existing security approaches are either ineffective or inefficient because of the permissionless requirement and the broadcasting overhead. We design and build a Lightweight and Identifier-Oblivious eNgine (LION) for the anomaly detection of the cryptocurrency networking. LION is not only effective in permissionless networking but is also lightweight and practical for the computation-intensive miners. We build LION for anomaly detection and use traffic analyses so that it minimally affects the mining rate and is substantially superior in its computational efficiency than the previous approaches based on machine learning. We implement a LION prototype on an active Bitcoin node to show that LION yields less than 1% of mining rate reduction subject to our prototype, in contrast to the state-of-the-art machine-learning approaches costing 12% or more depending on the algorithms subject to our prototype as well, while having detection accuracy of greater than 97% F1-score against the attack prototypes and real-world anomalies. LION therefore can be deployed on the existing miners without the need to introduce new entities in the cryptocurrency ecosystem.

【Keywords】Bitcoin; Prototypes; Peer-to-peer computing; Anomaly detection; Consensus protocol; Engines; Machine learning; Blockchain; cryptocurrency; bitcoin; P2P network; anomaly detection; statistical analysis; traffic analysis

【发表时间】2023 MAR-APR

【收录时间】2023-05-09

【文献类型】实证数据

【主题类别】

区块链治理-技术治理-加密流量监测