A Machine Learning Approach to Anomaly Detection Based on Traffic Monitoring for Secure Blockchain Networking-Reference-Cited by-同舟云学术

A Machine Learning Approach to Anomaly Detection Based on Traffic Monitoring for Secure Blockchain Networking

Author:

Kim Jinoh¹^ORCID,Nakashima Makiya¹,Fan Wenjun²^ORCID,Wuthier Simeon³^ORCID,Zhou Xiaobo³^ORCID,Kim Ikkyun⁴^ORCID,Chang Sang-Yoon³^ORCID

Affiliation:

1. Computer Science Department, Texas A&M University, Commerce, TX, USA

2. Department of Communications and Networking, School of Advanced Technology, Xi’an Jiaotong–Liverpool University, Jiangsu, Suzhou, China

3. Department of Computer Science, University of Colorado, Colorado Springs, CO, USA

4. Cybersecurity Research Division, ETRI, Daejeon, South Korea

Funder

National Science Foundation

Colorado State Bill

Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Networks and Communications

Link

Reference58 articles.

3. Anomaly detection in bitcoin network using unsupervised learning methods;pham;arXiv 1611 03941,2016

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