Improvement of cyber deffence information systems by adaptive technologies recognition of cyberattacks
Year: 2016
Type: article
Abstract: The last decade showed the rapid development of major-critical information systems (MCIS), where cyber tech-nology detection and identification of cyber-attacks are used for cyber defense. Necessity of further research in the development of methodological and theoretical foundations of information synthesis of self-learning cyber defense systems are caused by growing number destabilizing factors of cyber security of MCIS. This paper contains tasks of improving the stability of MCIS in terms of introduction of new systems and moderniza-tion of existing information and automated control sys-tems with increasing number of destabilizing effects on the availability, confidentiality and integrity of infor-mation.The process of cyber defense of MCIS is monitored and analyzed by values of several parameters of abnormalities signs or cyber-attacks. This is make it possible to carry out a preliminary assessment of information security via the clustering feature set of abnormalities or attempted cyber-attacks. Offered a categorical model of develop-ment adaptive systems of an intellectual detection of cyber threats (ASIDCT). Algorism of self-learning of ASIDCT is developed with the help of procedure of fuzzy clustering. This allows to create an adaptive self-learning mechanisms of ASIDCT. To assess the quality partitioning area of abnormalities signs, vulnerabilities and cyber-attacks is made a rational set of number of clusters and fuzziness index clusters in features area. It is proved that the offered approach gives the possibility to solve complex problems in control of cyber-attack pro-cess of MCIS and can be used in the development of software solutions for cyber defense systems.
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2564014331