Network Intrusion Detection for IoT Security

Abstract

The proliferation of IoT devices can be seen in every region of the world. The Dyn cyber-attack of 2016 revealed several vulnerabilities in modern intelligent networks. The safety of IoT devices is now paramount. The security of IoT is compromised when infected Internet-connected Things are deployed as botnets, but the whole Internet ecosystem is also at risk because of the potential for exploitation of these Things (smart gadgets). Mirai virus infiltrated security camera and brought the Internets to a grinding’s halts with DDoS assaults. Both the complexities and varieties of security attacks route have increased in recent years. Therefore, it is vital to study approaches in the context of the Internets of Things to discover and avoid or detect fresh assaults. This study analyzes the state of the art in IoT DNN (Deep Neural Networks) network security and provides a classification of the risks and issues faced by these networks. Since NIDSs are our major focus, this study examines the available NIDS implementation tools, datasets, and opensource and free network sniffer software.