Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

DaaS: Dew Computing as a Service for Intelligent Intrusion Detection in Edge-of-Things Ecosystem

Singh, Parminder and Kaur, Avinash and Aujla, Gagangeet Singh and Batth, Ranbir Singh and Kanhere, Salil (2021) 'DaaS: Dew Computing as a Service for Intelligent Intrusion Detection in Edge-of-Things Ecosystem.', IEEE Internet of Things Journal, 8 (16). pp. 12569-12577.

Abstract

Edge of Things (EoT) enables the seamless transfer of services, storage, and data processing from the cloud layer to edge devices in a large-scale distributed Internet of Things (IoT) ecosystems (e.g., Industrial systems). This transition raises the privacy and security concerns in the EoT paradigm distributed at different layers. Intrusion detection systems (IDSs) are implemented in EoT ecosystems to protect the underlying resources from attackers. However, the current IDSs are not intelligent enough to control the false alarms, which significantly lower the reliability and add to the analysis burden on the IDSs. In this article, we present a Dew Computing as a Service (DaaS) for intelligent intrusion detection in EoT ecosystems. In DaaS, a deep learning-based classifier is used to design an intelligent alarm filtration mechanism. In this mechanism, the filtration accuracy is improved (or sustained) by using deep belief networks. In the past, the cloud-based techniques have been applied for offloading the EoT tasks, which increases the middle layer burden and raises the communication delay. Here, we introduce the dew computing features that are used to design the smart false alarm reduction system. DaaS, when experimented in a simulated environment, reflects lower response time to process the data in the EoT ecosystem. The revamped DBN model achieved the classification accuracy up to 95%. Moreover, it depicts a 60% improvement in the latency and 35% workload reduction of the cloud servers as compared to edge IDS.

Item Type:Article
Full text:(AM) Accepted Manuscript
Download PDF
(702Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1109/JIOT.2020.3029248
Publisher statement:© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Date accepted:No date available
Date deposited:05 January 2022
Date of first online publication:07 October 2020
Date first made open access:05 January 2022

Save or Share this output

Export:
Export
Look up in GoogleScholar