Risk assessment for a video surveillance system based on Fuzzy Cognitive Maps

Abstract

For various IT systems security is considered a key quality factor. In particular, it might be crucial for video surveillance systems, as their goal is to provide continuous protection of critical infrastructure and other facilities. Risk assessment is an important activity in security management; it aims at identifying assets, threats and vulnerabilities, analysis of implemented countermeasures and their effectiveness in mitigating risks. This paper discusses an application of a new risk assessment method, in which risk calculation is based on Fuzzy Cognitive Maps (FCMs) to a complex automated video surveillance system. FCMs are used to capture dependencies between assets and FCM based reasoning is applied to aggregate risks assigned to lower-level assets (e.g. cameras, hardware, software modules, communications, people) to such high level assets as services, maintained data and processes. Lessons learned indicate, that the proposed method is an efficient and lowcost approach, giving instantaneous feedback and enabling reasoning on effectiveness of security system.

Combining Fuzzy Cognitive Maps and Discrete Random Variables

“…The proposed model aims at establishing ranges of activation levels reached during reasoning with FCMs. We were motivated by a particular problem of selecting accurate thresholds during IT security risk analysis with FCM [19,17,18], however, the presented here solution is more general and can be applied for a variety of problems. The FCM4DRV extension includes augmenting classical FCM state equation with appropriate operators applicable to DRVs, as well as introducing aggregators, special functions that transform DRVs into similar ones, yet less memory consuming and requiring smaller computational effort.…”

“…In this paper we propose an extension to the FCM model, named FCM4DRV, that aims at aggregating a number of reasoning tasks into a one parallel run. The described extension was motivated by the problem of qualitative evaluation of reasoning results for an FCM model of risks related to IT security [19,17,18], however, it is rather a general one, than tailored for a specific purpose. The idea behind FCM4DRV consists in replacing real-valued activation levels of concepts (and further influence weights) by random variables.…”

Evaluation of Cyber Security and Modelling of Risk Propagation with Petri Nets

“…These methods are appropriate and successful for evaluating the risk of some issues; however, they suffer from a lack of a formal mathematical method representing risk propagation among cooperative nodes in the system. Obviously, there are first works on modelling influences between assets and allowing their dependencies to be tracked during a risk aggregation, e.g., fuzzy cognitive maps were proposed by Szwed et al [15] in order to construct a hierarchical structure, in which components of a lower level deliver value to parent elements. Petri nets were successfully used by Henry et al [16] to assess the risk of SCADA system failure modes in terms of component or service malfunction.…”

A Dempster‐Shafer theory and uninorm‐based framework of reasoning and multiattribute decision‐making for surveillance system

“…Szwed et al propose an efficient and low‐cost risk assessment method to calculate risk based on fuzzy cognitive maps (FCMs) in a complex automated video surveillance system, which goal is to provide continuous protection of critical infrastructure and other facilities. In their model, FCMs are employed to model dependencies between assets and FCM‐based reasoning is employed to aggregate risks associated to assets.…”