A Novel Ensemble Learning Algorithm Based on D-S Evidence Theory for IoT Security

作     者：Changting Shi 

作者机构：College of Computer Science and TechnologyHarbin Engineering UniversityHarbin150001China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2018年第57卷第12期

页      面：635-652页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

主　　题：IoT security physical-layer security radio frequency fingerprinting random Forest evidence theory 

摘      要：In the last decade,IoT has been widely used in smart cities,autonomous driving and Industry 4.0,which lead to improve efficiency,reliability,security and economic ***,with the rapid development of new technologies,such as cognitive communication,cloud computing,quantum computing and big data,the IoT security is being confronted with a series of new threats and *** device identification via Radio Frequency Fingerprinting(RFF)extracting from radio signals is a physical-layer method for IoT *** physical-layer,RFF is a unique characteristic of IoT device themselves,which can difficultly be *** as people’s unique fingerprinting,different IoT devices exhibit different RFF which can be used for identification and *** this paper,the structure of IoT device identification is proposed,the key technologies such as signal detection,RFF extraction,and classification model is ***,based on the random forest and Dempster-Shafer evidence algorithm,a novel ensemble learning algorithm is *** theoretical modeling and experimental verification,the reliability and differentiability of RFF are extracted and verified,the classification result is shown under the real IoT device environments.