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Cyber Security Risk Assessment Model using Fuzzy Logic Greedy Inference Systems
Since computer networks first became widespread, the security of computer and networking systems has been an issue. Security issues have emerged calling for management to establish strategies to mitigate and reduce risks associated with each major shift of technology. Malicious acts and unintentional mistakes negatively impact organizations and individuals in many ways. In addition, cyber threats put serious threats to the integrity, confidentiality, and availability of data for the whole internet and intranet users.
Risk assessment and evaluation criteria have been applied by the organization to mitigate and reduce the vulnerability of risk into acceptable levels. It is known that different machine learning algorithms, for example support vector machine, genetic algorithm, neural network, data mining, fuzzy logic, and some others have been extensively applied to detect intrusion activities. In this session, our presenter will share the concepts of fuzzy logic inference system. These concepts are used to assess the risks and step involved in cybersecurity to control the attacks and compute the influence of security attack on network using fuzzy logic.
Fuzzy Inference System (FIS): The FIS was presented in 1965 by Lotfy Zadeh to support dealing with the problems that have ambiguous information. Therefore, exact values are used widely to approximate the reasoning in the events. Fuzzy logic is a multi-value logic which permits intermediate values to be defined between conventional ones like true/false, low/high, good/bad etc.
The fuzzy logic method has been employed in the risk valuation process in a lot of diversified examples & circumstances. It is a vital tool which can be automated and modelized to analyze the security. The tool by simulates various patterns including but not limited to Threats Analysis, Modeling the impact of cybercrime on Internet, and attack impact analysis. The method was originally established on an implication engine which was engaged to recognize potential risks to the computer-based systems. However, recent research efforts & results revealed its effectiveness in execution of threat modeling.
Presenter’s Bio: Mr. Anil Lamba is experienced leader with impressive industry credentials* and 15+ years of proven success in spearheading Strategic initiatives, Large-scale IT Infrastructure projects, IT Security Advisory & Risk Mgmt. Projects, Complex Information Security Audits & Governance Initiatives, Cloud Security Audits, Regulatory & Industry standard assessments, Mergers & Acquisitions projects, Transitions & Consolidation projects across industry verticals.
*Mr. Lamba's Industry Credentials: Ph.D. Cyber-Security (2019), M.B.A. – Strategic Project Management, CISA ®, CISP, PMP, AWS & AZURE Certified, Prince2, ITIL Expert, ISO 27001 Lead Auditor, MCSE, 6σ Sigma Green Belt, CEH and CCNA.