Anomaly Detection for Internet of Things Cyberattacks
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)]
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techniques have been proposed in the literature,existing detection methods address limited cyberattacks and utilize outdated datasets for *** this paper,we propose an intelligent,effective,and lightweight detection approach to detect several IoT *** proposed model includes a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%.