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

• Published in: Computers & electrical engineering Vol. 99; p. 107810
• Main Authors: Saba, Tanzila, Rehman, Amjad, Sadad, Tariq, Kolivand, Hoshang, Bahaj, Saeed Ali
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier Ltd 01.04.2022; Elsevier BV
• Subjects: Anomalies; Datasets; Deep learning; Internet of Things; Intrusion detection; Intrusion detection systems; IoT; Machine learning; Security; Smart village; Technological development; Traffic models; Deep learning; Technological development; Intrusion detection; Smart village; IoT; Anomalies
• ISSN: 0045-7906; 1879-0755
• DOI: 10.1016/j.compeleceng.2022.107810

•Categorized the attacks through the deep-learning method employing existing datasets.•Proposed a framework to add IDS as a program within IoT networks.•Designed a protection strategy for IoT network to maintain its integrity and provide its availability to legitimate users seamlessly. 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 model is trained and tested using the NID Dataset and BoT-IoT datasets and achieved an accuracy of 99.51% and 92.85%, respectively. [Display omitted]