EFFICIENCY OF THE INDICATORS INVESTMENT CALCULATION METHOD IN THE INFORMATION SECURITY SYSTEM OF INFORMATION OBJECTS
Open Access
- 1 January 2021
- journal article
- Published by Borys Grinchenko Kyiv University in Cybersecurity: Education, Science, Technique
- Vol. 1 (13), 16-28
- https://doi.org/10.28925/2663-4023.2021.13.1628
Abstract
The article describes the methodology of multi-criteria optimization of costs for the information protection system of the object of informatization. The technique is based on the use of a modified VEGA genetic algorithm. A modified algorithm for solving the MCO problem of parameters of a multi-circuit information protection system of an informatization object is proposed, which makes it possible to substantiate the rational characteristics of the ISS components, taking into account the priority metrics of OBI cybersecurity selected by the expert. In contrast to the existing classical VEGA algorithm, the modified algorithm additionally applies the Pareto principle, as well as a new mechanism for the selection of population specimens. The Pareto principle applies to the best point. At this point, the solution, interpreted as the best, if there is an improvement in one of the cybersecurity metrics, and strictly no worse in another metric (or metrics). The new selection mechanism, in contrast to the traditional one, involves the creation of an intermediate population. The formation of an intermediate population occurs in several stages. At the first stage, the first half of the population is formed based on the metric - the proportion of vulnerabilities of the object of informatization that are eliminated in a timely manner. At the second stage, the second half of the intermediate population is formed based on the metric - the proportion of risks that are unacceptable for the information assets of the informatization object. Further, these parts of the intermediate population are mixed. After mixing, an array of numbers is formed and mixed. At the final stage of selection for crossing, specimens (individuals) will be taken by the number from this array. The numbers are chosen randomly. The effectiveness of this technique has been confirmed by practical resultsKeywords
This publication has 20 references indexed in Scilit:
- Security Evaluation of Ring Oscillator PUF Against Genetic Algorithm Based Modeling AttackPublished by Springer Science and Business Media LLC ,2019
- Research on Hybrid Quantum Genetic Algorithm Based on Cross-Docking Delivery Vehicle SchedulingPublished by Springer Science and Business Media LLC ,2019
- Cyber attack detection and mitigation: Software Defined Survivable Industrial Control SystemsInternational Journal of Critical Infrastructure Protection, 2019
- Deep Learning Approach for Intelligent Intrusion Detection SystemIEEE Access, 2019
- Efficient Non-Dominated Multi-Objective Genetic Algorithm (NDMGA) and network security policy enforcement for Policy Space Analysis (PSA)Computer Communications, 2019
- Adaptive fuzzy genetic algorithm for multi biometric authenticationMultimedia Tools and Applications, 2019
- Multilayer Data-Driven Cyber-Attack Detection System for Industrial Control Systems Based on Network, System, and Process DataIEEE Transactions on Industrial Informatics, 2019
- Multi-Criteria Selection of Capability-Based Cybersecurity SolutionsPublished by HICSS Conference Office ,2019
- Ticket-based QoS routing optimization using genetic algorithm for WSN applications in smart gridJournal of Ambient Intelligence and Humanized Computing, 2018
- Design of Cyber Warfare TestbedPublished by Springer Science and Business Media LLC ,2018