Computers & Security, vol. 38, Elsevier, pp. 14-27, OCT 2013. DOI (I.F.: 1.172)
Any deliberate or unsuitable operational action in control tasks of critical infrastructures, such as energy generation, transmission and distribution systems that comprise sub-domains of a Smart Grid, could have a significant impact on the digital economy: without energy, the digital economy cannot live. In addition, the vast majority of these types of critical systems are configured in isolated locations where their control depends on the ability of a few, supposedly trustworthy, human operators. However, this assumption of reliabilty is not always true. Malicious human operators (criminal insiders) might take advantage of these situations to intentionally manipulate the critical nature of the underlying infrastructure. These criminal actions could be not attending to emergency events, inadequately responding to incidents or trying to alter the normal behaviour of the system with malicious actions. For this reason, in this paper we propose a smart response mechanism that controls human operators’ operational threats at all times. Moreover, the design of this mechanism allows the system to be able to not only evaluate by itself, the situation of a particular scenario but also to take control when areas are totally unprotected and/or isolated. The response mechanism, which is based on Industrial Wireless Sensor Networks (IWSNs) for the constant monitoring of observed critical infrastructures, on reputation for controlling human operators’ actions, and on the ISA100.11a standard for alarm management, has been implemented and simulated to evaluate its feasibility for critical contexts.
5th International conference on Critical Information Infrastructures Security (CRITIS’10), LNCS 6712, Springer, pp. 55-67, September, 2010.
A way of controlling a cascading effect caused by a failure or a threat in a critical system is using intelligent mechanisms capable of predicting anomalous behaviours and also capable of reacting against them in advance. These mechanisms are known as Early Warning Systems (EWS) and this will be precisely the main topic of this paper. Specially, we present an EWS design based on a Wireless Sensor Network (using the ISA100.11a standard) that constantly supervise the application context. This EWS is also based on forensic techniques to provide dynamic learning capacities. As a result, this new approach will aid to provide a reliable control of incidences by offering a dynamic alarm management, identification of the most suitable field operator to attend an alarm, reporting of causes and responsible operators, and learning from new anomalous situations.