Enhanced Security with Deep Learning-Based Intrusion Detection

The Internet of Things (IoT) is widely used in modern culture, particularly in intelligent transportation systems and smart homes. According to IHS Markit, there will be 125 billion linked devices by 2030, up from 27 billion in 2017. That equates to an average annual growth rate of 12%. The design of the current Internet of Things, the vast number of linked devices, the variety of connection types (mainly wireless), and the massive volumes of data transferred over the network raise serious security issues. Numerous new technical security risks are emerging as a result of the Internet of Things. Although there are several approaches available for protecting IoT networks, more is still required. We suggest using deep learning to improve it. Denial-of-service (DOS), distributed denial-of-service (DDOS), probing, man-in-the-middle (spoofing), and remote-to-local attacks are among the potential attacks that the current approach should be able to detect. The proposed DL-IDS (Deep Learning-Intrusion Detection System) performs better than its predecessors in terms of recall, accuracy, and precision, according to research.