Anomaly Detection, Analysis and Prediction Techniques in IoT Environment: A Systematic Literature Review

Muhammad Fahim*, Alberto Sillitti

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

27 Citations (Scopus)

Abstract

Anomaly detection has attracted considerable attention from the research community in the past few years due to the advancement of sensor monitoring technologies, low-cost solutions, and high impact in diverse application domains. Sensors generate a huge amount of data while monitoring the physical spaces and objects. These huge collected data streams can be analyzed to identify unhealthy behaviors. It may reduce functional risks, avoid unseen problems, and prevent downtime of the systems. Many research methodologies have been designed and developed to determine such anomalous behaviors in security and risk analysis domains. In this paper, we present the results of a systematic literature review about anomaly detection techniques except for these dominant research areas. We focus on the studies published from 2000 to 2018 in the application areas of intelligent inhabitant environments, transportation systems, health care systems, smart objects, and industrial systems. We have identified a number of research gaps related to the data collection, the analysis of imbalanced large datasets, limitations of statistical methods to process the huge sensory data, and few research articles in abnormal behavior prediction in real scenarios. Based on our analysis, researchers and practitioners can acquaint themselves with the existing approaches, use them to solve real problems, and/or further contribute to developing novel techniques for anomaly detection, prediction, and analysis.

Original languageEnglish
Pages (from-to)81664-81681
Number of pages18
JournalIEEE Access
Volume7
Early online date10 Jun 2019
DOIs
Publication statusPublished - 03 Jul 2019
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.

Keywords

  • industrial systems
  • intelligent environments
  • intelligent transportation systems
  • machine learning
  • smart objects
  • Statistical learning

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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