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

Singh, Parminder; Kaur, Avinash; Aujla, Gagangeet Singh; Batth, Ranbir Singh; Kanhere, Salil

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Parminder Singh

Avinash Kaur

Ranbir Singh Batth

Salil Kanhere


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.


Singh, P., Kaur, A., Aujla, G. S., Batth, R. S., & Kanhere, S. (2021). DaaS: Dew Computing as a Service for Intelligent Intrusion Detection in Edge-of-Things Ecosystem. IEEE Internet of Things Journal, 8(16), 12569-12577.

Journal Article Type Article
Online Publication Date Oct 7, 2020
Publication Date 2021-08
Deposit Date Apr 27, 2021
Publicly Available Date Jan 5, 2022
Journal IEEE Internet of Things Journal
Electronic ISSN 2372-2541
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 8
Issue 16
Pages 12569-12577


Accepted Journal Article (718 Kb)

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