Information Security Evaluation of Industrial Control Systems Using Probabilistic Linguistic MCDM Method

Industrial control systems (ICSs) are widely used in various fields, and the information security problems of ICSs are increasingly serious. The existing evaluation methods fail to describe the uncertain evaluation information and group evaluation information of experts. Thus, this paper introduces the probabilistic linguistic term sets (PLTSs) to model the evaluation information of experts. Meanwhile, we propose a probabilistic linguistic multi-criteria decision-making (PL-MCDM) method to solve the information security assessment problem of ICSs. Firstly, we propose a novel subscript equivalence distance measure of PLTSs to improve the existing methods. Secondly, we use the Best Worst Method (BWM) method and Criteria Importance Through Inter-criteria Correlation (CRITIC) method to obtain the subjective weights and objective weights, which are used to derive the combined weights. Thirdly, we use the subscript equivalence distance measure method and the combined weight method to improve the probabilistic linguistic Visekriterijumska Optimizacija I Kompromisno Resenje (PL-VIKOR) method. Finally, we apply the proposed method to solve the information security assessment problem of ICSs. When comparing with the existing methods such as the probabilistic linguistic Tomada deDecisao Iterativa Multicriterio (PL-TODIM) method and probabilistic linguistic Technique for Order Preference by Similarity to Ideal Solution (PL-TOPSIS) method, the case example shows that the proposed method can provide more reasonable ranking results. By evaluating and ranking the information security level of different ICSs, managers can identify problems in time and guide their work better.