Modern Critical infrastructures have command and control systems. These command and control systems are commonly called supervisory control and data acquisition (SCADA). In the past, SCADA system has a closed operational environment, so these systems were designed without security functionality. Nowadays, as a demand for connecting the SCADA system to the open network growths, the study of SCADA system security is an issue. A keymanagement scheme is critical for securing SCADA communications. Numerous key-management structures for SCADA also have been suggested. 11770-2 Mechanism 9 Key establishment Protocol has been used in SCADA communication however a security proof for the 11770-2 Mechanism 9 protocol is needed. The purpose of this paper is to provide a general overview about SCADA system, and its related security issues. Furthermore, we try to investigate the importance of key management protocol and the need of formal security poof.
The paper presents recent results on the application of soft computing techniques for predictive modelling in the built sector. More specifically, an air-conditioned zone (Anglesea Building, University of Portsmouth), a naturally ventilated room (Portland Building, University of Portsmouth), and an endothermic building (St Catherine's Lighthouse, Isle of Wight) are considered. The zones are subjected to occupancy effects and external disturbances which are difficult to predict in a quantitative way and hence the soft computing approach seems to be a better alternative. In fact, the overall complexity of the problem domain makes the modelling of the internal climate in buildings a difficUlt task which is not always carried out in a satisfactory way by traditional deterministic and stochastic methods. The approach adopted uses fuzzy logic for modelling, as well as neural networks for adaptation and genetic algorithms for optimisation of the fuzzy model. The latter is of the Takagi-Sugeno type and it is built by subtractive clustering as a result of which the initial values of the antecedent non-linear membership functions and the consequent linear algebraic equations parameters are determined. A method of a combinatorial search over all possible fuzzy model structures for a specified plant order is presented. The model parameters are further adjusted by a back-propagation neural network and a real-valued genetic algorithm in order to obtain a better fit to the measured data. Modelling results with actual data from the three buildings are presented where the initial (fuzzy) and the final (fuzzy-neuro andfuzzy-genetic) models are shown.
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