An Artificial Intelligence based Design and Implementation for classifying the Missing Data in IoT Applications
Balasaheb Bhamangol, Amit Kaiwade, Bhasker Pant, Arti Rana, Abhijeet Kaiwade, Atik Shaikh · 2022
The Industrial Internet of Things (IIoT) has become one of the most rapidly developing innovative technologies in recent years, with the potential to digitize and connect numerous industries for enormous commercial prospects and growth of the global GDP. Even while the IIoT offers exciting potential for the creation of many industrial applications, these applications must meet more stringent security requirements and are vulnerable to cyberattacks. Since there are so many sensors in the IIoT network, a lot of data is produced, which has caught the attention of hackers all around the world. Network activity is monitored by the intrusion detection system (IDS), which recognizes network behavior is regarded as is a crucial security measure for protecting IIoT applications from threats. Deep learning and machine learning approaches have recently demonstrated their ability to improve intrusion detection performance and reduce a variety of security concerns. The research’s ultimate goal is to pinpoint the shortcomings and difficulties of previous studies as well as potential remedies and directions in futures.