The increased use of IndustrialWireless Sensor Networks (IWSN) in a variety of different applications, including those that involve critical infrastructure, has meant that adequately protecting these systems has become a necessity. These cyber-physical systems improve the monitoring and control features of these systems but also introduce several security challenges. Intrusion detection is a convenient second line of defence in case of the failure of normal network security protocols. Anomaly detection is a branch of intrusion detection that is resource friendly and provides broader detection generality making it ideal for IWSN applications. These schemes can be used to detect abnormal changes in the environment where IWSNs are deployed. This paper presents a literature survey of the work done in the field in recent years focusing primarily on machine learning techniques. Major research gaps regarding the practical feasibility of these schemes are also identified from surveyed work and critical water infrastructure is discussed as a use case.
Reference:
Ramotsoela, D., Abu-Mahfouz, A.M.I. and Hancke, G.P. 2018. A survey of anomaly detection in Industrial Wireless Sensor Networks with critical water system infrastructure as a case study. Sensors, vol. 18(8): doi:10.3390/s18082491
Ramotsoela, D., Abu-Mahfouz, A. M., & Hancke, G. (2018). A survey of anomaly detection in Industrial Wireless Sensor Networks with critical water system infrastructure as a case study. http://hdl.handle.net/10204/10617
Ramotsoela, D, Adnan MI Abu-Mahfouz, and GP Hancke "A survey of anomaly detection in Industrial Wireless Sensor Networks with critical water system infrastructure as a case study." (2018) http://hdl.handle.net/10204/10617
Ramotsoela D, Abu-Mahfouz AM, Hancke G. A survey of anomaly detection in Industrial Wireless Sensor Networks with critical water system infrastructure as a case study. 2018; http://hdl.handle.net/10204/10617.
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).