21 May 2015 An asset valuation approach using fuzzy logic
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Abstract
Organisations that own operational networks have many networked assets to support daily business. For meaningful decision support on these assets and their services, network defenders need to know the values of their assets and services. Unfortunately, there is no easy way of knowing or determining these values, and no universally recognised approach to asset valuation exists. Proprietary and published approaches, mostly in risk analysis, tend to assume values whose significance may be hard to justify in practice. Fortunately, experienced computer security experts can give intuitive guidance on the relative importance of network assets in operational networks. Such experiential knowledge, though difficult to quantify through classical relational mathematics, can be generally effective in assigning relative values to assets. In this work, we propose to capitalise on this expertise by combining asset attribute factors with expert and experiential knowledge about assets to determine their values. We exploit the mathematical theory of fuzzy logic that can be used to model and quantify human expertise and experiential knowledge. Our approach starts by modeling experts' experiential knowledge about assets and their properties as fuzzy variables. Then we use a fuzzy inference system to translate that knowledge into an asset value. Our results show asset values that are a close match with what an experienced expert would infer local asset values to be.
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Henry Leung, Henry Leung, } "An asset valuation approach using fuzzy logic", Proc. SPIE 9498, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2015, 949802 (21 May 2015); doi: 10.1117/12.2177533; https://doi.org/10.1117/12.2177533
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