Skip to main content
SpringerLink
Log in

Anomaly Detection for IoT Systems

  • Living reference work entry
  • First Online:
Encyclopedia of Wireless Networks

  • 310 Accesses

Synonyms

Anomaly; Internet of Things (IoT)

Definitions

Anomaly detection for Internet of Things (IoT) system is to automatically detect whether the IoT devices, components, or systems operate normally or not. Usually there are multiple sensors or external monitors that observe the signals sent from the operating IoT systems. The detection module analyzes the signals to determine whether the system’s behavior is normal or abnormal.

Historical Background

The IoT systems link the heterogeneous sensors and IoT servers to provide the IoT applications such as healthcare, industrial automation, environment monitoring, and so on. Because the decade aged IoT systems and new IoT systems may coexist, it is not easy to implement the monitors into the integrated IoT systems. It is usually to treat the integrated IoT system as a black box. Furthermore, because the signals come from one or more types of sensors (i.e., heterogeneous sensors), it is complicated for the monitors to analyze the signals...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Reference

  • Breunig MM, Kriegel H-P, Ng RT, Sander J (2000) LOF: identifying density-based local outliers. ACM SIGMOD Rec 29(2):93–104

    Article  Google Scholar 

  • Difallah DE, Cudre-Mauroux P, McKenna SA (2013) Scalable anomaly detection for smart city infrastructure networks. IEEE Internet Comput 17(6):39–47

    Article  Google Scholar 

  • Fahad LG, Rajarajan M (2015) Anomalies detection in smart-home activities. In: Proceedings of IEEE international conference on machine learning and applications

    Google Scholar 

  • Hodo E, Bellekens X, Hamilton A, Dubouilh PL, Iorkyase E, Tachtatzis C, Atkinson R (2016) Threat analysis of IoT networks using artificial neural network intrusion detection system. In: Proceedings of IEEE international symposium on networks, computers and communications

    Google Scholar 

  • Juvonen A, Sipola T, Hämäläinen T (2015) Online anomaly detection using dimensionality reduction techniques for http log analysis. Comput Netw Int J Comput Telecommun Netw 91:46–56

    Google Scholar 

  • Kwak BI, Woo J, Kim HK (2016) Know your master: driver profiling-based anti-theft method. In: Proceedings of IEEE annual conference on privacy, security and trust

    Google Scholar 

  • Porkodi R, Bhuvaneswari V (2014) The internet of things applications and communication enabling technology standards: an overview. In: Proceedings of the international conference on intelligent computing applications

    Google Scholar 

  • Shin JK, Lee B, Park KS (2011) Detection of abnormal living patterns for elderly living alone using support vector data description. IEEE Trans Inf Technol Biomed 15(3):438–448

    Article  Google Scholar 

  • Stojanovic L, Dinic M, Stojanovic N, Stojadinovic A (2016) Big-data-driven anomaly detection in industry (4.0): an approach and a case study. In: Proceedings of IEEE international conference on big data

    Google Scholar 

  • Valenzuela J, Wang J, Bissinger N (2013) Real-time intrusion detection in power system operations. IEEE Trans Power Syst 28(2):1052–1062

    Article  Google Scholar 

  • Xie M, Hu J, Guo S, Zomaya AY (2017) Distributed segment-based anomaly detection with Kullback–Leibler divergence in wireless sensor networks. IEEE Trans Inf Forensics Secur 12(1):101–110

    Article  Google Scholar 

  • Zhang Z, Wang X, Lin S (2016) Mobile payment anomaly detection mechanism based on information entropy. IET Netw 5(1):1–7

    Article  Google Scholar 

  • Zong B, Song Q, Min MR, Cheng W, Lumezanu C, Cho D, Chen H (2018) Deep autoencoding gaussian mixture model for unsupervised anomaly detection. In: Proceedings of international conference on learning representations

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

    Xin-Xue Lin, En-Hau Yeh & Phone Lin

Authors
  1. Xin-Xue Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. En-Hau Yeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Phone Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xin-Xue Lin .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Waterloo , Waterloo, ON, Canada

    Xuemin (Sherman) Shen

  2. Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, ON, Canada

    Xiaodong Lin

  3. Dpt.of Electrical & Computer Engineering, University of Nebraska–Lincoln, Omaha, NE, USA

    Kuan Zhang

Section Editor information

  1. Taipei, Taiwan

    Phone Lin

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Lin, XX., Yeh, EH., Lin, P. (2019). Anomaly Detection for IoT Systems. In: Shen, X., Lin, X., Zhang, K. (eds) Encyclopedia of Wireless Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-32903-1_183-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-32903-1_183-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32903-1

  • Online ISBN: 978-3-319-32903-1

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation