Friday 24th of November 2017
 

Optimizing Risk Management Using Learning Automata


Mohhamad Reza Ahmadi, Babak Anari, Mostafa Gobaye Arani and Zohreh Anari

nowadays, information systems constitute a crucial part of organizations; by losing security, these organizations will loss plenty of competitive advantages as well. A healthful company has to anticipate undesired events by defining a process for managing risks. Risk management processes are responsible for identifying, analyzing and evaluating risky scenarios and whether they should undergo control in order to satisfy a previously defined risk criterion. Risk specialists have to consider, at the same time, many operational aspects (decision variables) and objectives to decide which and when risk treatment have to be executed. Aiming to balancing the competition between risk and resource management this paper proposes a new optimization step within the standards risk management methodology created by the International organization for Standardization. Our objective is to automatically find a subset of risks that maximize risk reduction and respect the company operational resource limitations. This paper applied a Learning Automaton (LA) for risk reduction in uncertainly. To test the resulted methodology, experiments based on the Simple selection algorithm were performed aiming to manage risk and resources of a simulated company. Result show us that the proposed approach can deal with multiple conflicting objectives reducing the risk exposure time by selecting risks to be treated according their impact, and available resources.

Keywords: Learning Automaton, Risk management, Risk optimization

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ABOUT THE AUTHORS

Mohhamad Reza Ahmadi
Research Institute of ICT, tehran-iran

Babak Anari
Islamic Azad University of shabestar

Mostafa Gobaye Arani
Islamic Azad University of kashan

Zohreh Anari
payame noor shabestar


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