Software security risk analysis using fuzzy

Second, this approach is not always justified. An information systems security risk assess- ment model under the Dempster-Shafer theory of belief functions.

These techniques provide a representation of system operations and undesirable events and validation of system safety level [12, 24, 8, 30, 9]. The solution is calculated based on the Bellman—Zadeh principle [34]: Now, go and apply that to your organizational goals. The main methods for processing such risks are risk avoiding the complete elimination of the risk sourcesassignment insuranceand reduction—choice and intro- duction of protective measures.

The big data concept and its applications have emerged from the increasing volumes of external and internal data in organizations and it differs from other databases in four aspects: Abstract Big data is the term used to denote enormous sets of data that differ from other classic databases in four main ways: Values 1 and 0 are used for positive and negative answers.

Reference [ 15 ] proposed a new type of digital signature that is specifically designed for a graph-based big data system. These characteristics criteria are used in the example for firewall evaluation.

Problems with scoring methods and ordinal scales in risk assessment. OR I can simply work to demonstrate that productivity increases shareholder value, and linking that productivity to various proven KPIs which may not have an ROI, but they'll certainly illustrate a point.

Can you say with any certainty that less vulnerabilities leads to less breaches.

Network Security Risk Assessment Based on Fuzzy Analytical Hierarchy Process

In a hospital, software security increases reliability and availability - which leads to a higher patient survivability rate. Show Context Citation Context Journal of Loss Prevention in the Process Industries,12, 85— An application of a new method of risk analysis to an e-health system of monitoring vital signs was discussed in [23].

Auerbach Publications Next, we introduce the methodology and present a real case that illustrates how the methodology validates the proposed approach.

The contr ibution of organiz ational i mage s of i nfor mation s y stem s ecuri ty to the implem entation of sec ure i nformati on s y ste ms Several risk assessment processes are defined by over 15 standards or methods [2], including most popular: This leads to a substantial gap: To illustrate the calculations we marked in Table 1 obtained answers to the questionnaire by using underlined, bold font.

The range definition is shown in table above. Membership functions for Confidentiality Similarly, the output, that is, the level of security risk is also represented by fuzzy sets and then a membership function. As a basic tool we use a questionnaire, in which various involved stakeholders reply to questions concerning applied countermeasures.

We have been able to design a system that can be used to evaluate the security risk associated with the production of secure software systems.

Institute for Computer Sciences and Technology That would be all well and good, if the math wasn't all fuzzy.

Software Security Risk Analysis Using Fuzzy Expert System

This will definitely help software organizations meet up with the standard requirements. It should be observed, that a questionnaire defines in fact a structure of a Fuzzy Cognitive Map, in which weights express influences. Do you suddenly get faster development cycles.

However, for many IT systems it is difficult to estimate financial loss caused by potential failures, as it requires the specification of a business environment, in which the system is deployed.

Discrete Choice Theory of Product Differ- entiation.

Mathematical Problems in Engineering

Hierarchical threat assessment and quantitative calculation method of network security threatening state, Journal of Software 17 4: Article Recommendations Abstract For contemporary software systems, security is considered to be a key quality factor and the analysis of IT security risk becomes an indispensable stage during software deployment.

Neuro-Fuzzy based Software Risk Estimation Tool Keywords: software security, software threat, neural network, fuzzy logic, neuro-fuzzy. GJCST-C Classification: D analysis model is proposed using Hidden Markov Model (HMM), to forecast the Cyber threat trend.

HMM is a tool. Using Fuzzy Logic to Increase the Accuracy of E-Commerce Risk Assessment Based on an Expert System Strong adaptive control can be exercised even without access to accurate data inputs.

Such control is possible through fuzzy mathematics, which is a meta-collection of Boolean logic principles that imply relative accuracy. multidimensional approach to information security risk management using FMEA and fuzzy theory of information security are assessed using fuzzy num-bers, The second type, based on quantitative risk analysis methods, contains mathematical instruments to evaluate risk and,inthiscase,mathematicalprocedures,suchasfuzzylogic.

Risk management is widely seen as the basis for cybersecurity in contemporary organizations, but practitioners continue to dispute its value. This article analyzes debate over computer security risk management in the s and s United States, using this debate to enhance our understanding of the value of computer security metrics more generally.

M.S. in GIST Theses * indicates thesis award winner Jo-Anne Antoun Cartographic Design and Interaction: An Integrated User-Centered Agile Software Development Framework for.


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Software security risk analysis using fuzzy
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