Intrusion detection models for IOT networks via deep learning approaches. (February 2023)

• Record Type: Journal Article
• Authors: Madhu, Bhukya; Venu Gopala Chari, M.; Vankdothu, Ramdas; Silivery, Arun Kumar; Aerranagula, Veerender
• Is Part Of: Measurement. Volume 25(2023)
• Publication Date: 2023-02
• Subjects: Machine learning/ deep learning -- IoT -- Device based intrusion detection system -- Network disruption; Detectors -- Periodicals; Measurement -- Periodicals; 530.7
• DOI: 10.1016/j.measen.2022.100641
• Languages: English
• ISSNs: 2665-9174

Abstract

Abstract: The Internet of things (IoT) has gained more attention in recent years because of its ubiquitous operations, connectivity, methods of communication, and intelligent decisions to evoke activities from various devices. Therefore, artificial intelligence techniques have been integrated into all aspects of the Internet of Things and making life more comfortable in various ways. A novel deep learning model named Device-based Intrusion Detection System (DIDS) was proposed in the second phase. This DIDS learning model incorporates the prediction of unknown attacks to handle the computational overhead in large networks and increase the throughput with a low false alarm rate. Our proposed algorithm has been evaluated with standard algorithms, and the results show that it detects attacks earlier than standard algorithms. The computational time has also been reduced, and 99% of accuracy has been achieved in detecting the attacks.