Anomaly-based intrusion detection system for IoT networks through deep learning model

By Tanzila Saba, Amjad Rehman, Tariq Sadad, H. Kolivand, Saeed Ali Omer Bahaj. Computers & electrical engineering, 2022

378 citations recorded by the source corpus.

: The Internet of Things (IoT) idea has been developed to enhance people's lives by delivering a diverse range of smart interconnected devices and applications in several domains. However, security threats are main critical challenges for the devices in an IoT environment. Many approaches have been proposed to secure IoT appliances in state of the art, still advancement is desirable. Machine learning has demonstrated a capability to detect patterns when other methodologies have collapsed. One advanced method to enhance IoT security is to employ deep learning. This formulates a seamless option for anomaly-based detection. This paper presents a CNN-based approach for anomaly-based intrusion detection systems (IDS) that takes advantage of IoT's power, providing qualities to efficiently examine whole traffic across the IoT. The proposed model shows ability to detect any possible intrusion and abnormal traffic behavior. The

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/7fac16246122a4ac00be16fd4487ca5ad2559eb1