A New Intrusion Detection System for the Internet of Things via Deep Convolutional Neural Network and Feature Engineering.

ABSTRACT: The Internet of Things (IoT) is a widely used technology in automated network systems across the world. The impact of the IoT on different industries has occurred in recent years. Many IoT nodes collect, store, and process personal data, which is an ideal target for attackers. Several researchers have worked on this problem and have presented many intrusion detection systems (IDSs). The existing system has difficulties in improving performance and identifying subcategories of cyberattacks. This paper proposes a deep-convolutional-neural-network (DCNN)-based IDS. A DCNN consists of two convolutional layers and three fully connected dense layers. The proposed model aims to improve performance and reduce computational power. Experiments were conducted utilizing the IoTID20 dataset. The perfor

SUBMITTER: Ullah S 

PROVIDER: S-EPMC9146064 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature