The increased use of Industrial Wireless 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.
Kobo, Hlabishi Isaac; Abu-Mahfouz, Adnan Mohammed; Hancke, Gerhardus P. (Jr.)(Institute of Electrical and Electronics Engineers, 2019-02)
Software-defined wireless sensor networks (WSNs) are a new and emerging network paradigm that seeks to address the impending issues in WSNs. It is formed by applying software-defined networking to WSNs whose basic tenet ...
Onumanyi, A.J. (Adeiza); Abu-Mahfouz, Adnan Mohammed; Hancke, Gerhard P.(Wiley, 2019-10)
Modern energy detectors typically use adaptive threshold estimation algorithms to improve signal detection in cognitive radio–based industrial wireless sensor networks (CR‐IWSNs). However, a number of adaptive threshold ...
Van Rhyn, Pierre; Hancke, Gerhard P.(Elsevier, 2017-02)
A novel method is proposed to estimate committed information rate (CIR) variations in typical orthogonal frequency division multiplexing
(OFDM) wireless local area networks (WLANs) that are applied in wireless sensor ...