Fuzzy cognitive map-based reasoning for prediction of multi-stage attacks in risk assessment

Sarala, R.; Zayaraz, G.; Vijayalakshmi, V.

doi:10.1504/ijiei.2016.076700

Cited by 3 publications

(3 citation statements)

References 21 publications

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“…They also applied FCM to predict the impact of risks on the outcomes of these projects. Sarala et al [138] utilized FCM to predict multi-stage attacks and to model the causally dependent events in the field of computer science-related problems.…”

Section: Hybridmentioning

confidence: 99%

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Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Bakhtavar

Valipour

Yousefi

et al. 2020

Complex Intell. Syst.

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Fuzzy cognitive maps (FCMs) have been widely applied to analyze complex, causal-based systems in terms of modeling, decision making, analysis, prediction, classification, etc. This study reviews the applications and trends of FCMs in the field of systems risk analysis to the end of August 2020. To this end, the concepts of failure, accident, incident, hazard, risk, error, and fault are focused in the context of the conventional risks of the systems. After reviewing risk-based articles, a bibliographic study of the reviewed articles was carried out. The survey indicated that the main applications of FCMs in the systems risk field were in management sciences, engineering sciences and industrial applications, and medical and biological sciences. A general trend for potential FCMs’ applications in the systems risk field is provided by discussing the results obtained from different parts of the survey study.

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Section: Hybridmentioning

confidence: 99%

“…Information Science (9.37%) [82,50],Chang [80], Lee et al (2012); [149], [84], [59], [138], [ papers, respectively. These two authors as well as Groumpos PP and Stylios CD have been known as the top researchers in the field of FCMs in general.…”

mentioning

confidence: 99%

Fuzzy cognitive maps in systems risk analysis: a comprehensive review

Bakhtavar

Valipour

Yousefi

et al. 2020

Complex Intell. Syst.

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“…Despite the dynamic capabilities and their practical implementation, the first PI-fuzzy suffered from lack of stability analysis, that is to say the stability of the closed loop is not guaranteed (Sarala et al, 2016;Furuhashi et al, 1998;Faquir et al, 2016). Recently, a large class of adaptive fuzzy systems, which use the fuzzy systems of Mamdani or TS was developed to control nonlinear processes, and stability has been guaranteed in the sense of the Lyapunov theory.…”

Section: Introductionmentioning

confidence: 99%

Speed control of a doubly-fed induction machine based on fuzzy adaptive

Saidi

Naceri

2019

IJIEI

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In this paper, we are interested in the adaptive fuzzy control a technique has been studied and applied, namely adaptive fuzzy control based on theory of Lyapunov. The system based on the stability theory is used to approximate the gains k e and k dce to ensure the stability of the control in particular time, simulations results obtained by using MATLAB environment gives that the fuzzy adaptive control more robust, also it has superior dynamics performances. The results and test of robustness will be presented.

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