An Integrated Framework for Enhancing Security and Privacy in IoT-Based Business Intelligence Applications

Randhir Kumar, Prabhat Kumar, Alireza Jolfaei, A. K.M.Najmul Islam

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)

Abstract

Business intelligence (BI) is the procedure of strategically planning and using a variety of tools and techniques to get important data insights and make wise business decisions. The Internet of Things (IoT) has emerged as the main source of big data with the most real-time data used across all business sectors in recent years. However, the expansion of business results is impacted by the integration of IoT as data sources with conventional BI systems. This is because there are now more security and privacy concerns in IoT ecosystems as a result of adversaries undertaking data inference and poisoning attacks on networked IoT devices via the open communication medium Internet. This study proposes an integrated architecture for strengthening security and privacy in IoT-based BI applications, which is inspired by the discussion above. The suggested structure contains two engines: an intrusion detection engine and a two-level privacy engine. Due to adversaries conducting data inference and poisoning attacks on networked IoT devices over the open communication medium Internet, there are now additional security and privacy risks in IoT ecosystems. Based on the discussion above, this study suggests an integrated architecture for enhancing security and privacy in IoT-based BI applications. The two different engines are designed namely two-level privacy and intrusion detection engine. The experimental outcomes utilising the real IoT-based dataset ToN-IoT show that the suggested strategy outperforms previous peer privacy-preserving machine learning algorithms in terms of detection rate, accuracy, F1 score, and precision.

Original languageEnglish
Title of host publication2023 IEEE International Conference on Consumer Electronics, ICCE 2023
PublisherInstitute of Electrical and Electronics Engineers
Number of pages19
ISBN (Electronic)9781665491303
DOIs
Publication statusPublished - 17 Feb 2023
Event2023 IEEE International Conference on Consumer Electronics, ICCE 2023 - Las Vegas, United States
Duration: 6 Jan 20238 Jan 2023

Publication series

NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
Volume2023-January
ISSN (Print)0747-668X

Conference

Conference2023 IEEE International Conference on Consumer Electronics, ICCE 2023
Country/TerritoryUnited States
CityLas Vegas
Period6/01/238/01/23

Keywords

  • Blockchain Technology
  • Business Intelligence
  • Internet of Things (IoT)
  • Intrusion Detection System (IDS)
  • Machine Learning
  • Privacy-Preservation

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