期刊名称：International Journal of Advances in Engineering and Management
电子版ISSN：2395-5252
出版年度：2020
卷号：2
期号：7
页码：303-308
DOI：10.35629/5252-0207216223
语种：English
出版社：IJAEM JOURNAL
摘要：The Internet of Things (IoT) combines hundreds of millions of devices which are capable of interaction with each other with minimum user interaction. Now internet of things is become the fastest-growing areas in of computing; however, the reality is that in the extremely hostile environment of the internet, IoT is vulnerable to numerous types of cyber attacks. To resolve this, Intrusion detection in the internet of things (IOT) is a rising concern practical countermeasures need to be established to secure IoT networks, such as network intrusion detection. Since IoT devices have low storage capacity and low processing power, traditional high-end security solutions to protect an IoT system are not appropriate. Also, IoT devices are now connected without human intervention for longer periods. This implies that intelligent network-based security solutions like machine learning solutions must be developed. This work proposed a new approach for classification of cyber attack. Machine learning technique also used for this system. this work talks about classification of abnormal activity, high accuracy and detection rate with low false alarm.
关键词：Internet of Things;IDS;Classification;Machine Learning;IoT;Detection rate
