Anomaly Detection for Internet of Things Cyberattacks

作     者：Manal Alanazi Ahamed Aljuhani 

作者机构：College of Computing and Information TechnologyUniversity of TabukTabuk71491Saudi Arabia 

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

年 卷 期：2022年第72卷第7期

页      面：261-279页

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The authors would like to thank Dr. Okba Taouali and Dr. Mohammed Mustafa for their insightful comments om how we could improve the quality of paper. We greatly appreciate the time and effort they have dedicated to improving the manuscript 

主　　题：Anomaly detection anomaly-based IDS cybersecurity feature selection Internet of Things(IoT) intrusion detection 

摘      要：The Internet of Things(IoT)has been deployed in diverse critical sectors with the aim of improving quality of service and facilitating human *** IoT revolution has redefined digital services in different domains by improving efficiency,productivity,and *** service providers have adapted IoT systems or plan to integrate them as integral parts of their systems’operation;however,IoT security issues remain a significant *** minimize the risk of cyberattacks on IoT networks,anomaly detection based on machine learning can be an effective security solution to overcome a wide range of IoT *** various detection techniques have been proposed in the literature,existing detection methods address limited cyberattacks and utilize outdated datasets for *** this paper,we propose an intelligent,effective,and lightweight detection approach to detect several IoT *** proposed model includes a collaborative feature selection method that selects the best distinctive features and eliminates unnecessary features to build an effective and efficient detection *** the detection phase,we also proposed an ensemble of learning techniques to improve classification for predicting several different types of IoT *** experimental results show that our proposed method can effectively and efficiently predict several IoT attacks with a higher accuracy rate of 99.984%,a precision rate of 99.982%,a recall rate of 99.984%,and an F1-score of 99.983%.