DOI: https://doi.org/10.20535/2411-1031.2017.5.1.120554

Analysis of existing solutions for preventing invasion in information and telecommunication networks

Igor Subach, Vitalii Fesokha, Nadiia Fesokha

Abstract


The article presents an overview of the current state of cybernetic space in the context of the growth of cybercrime (large-scale cyber attacks, which have received wide publicity in Ukraine and the world). A comparative analysis of the main existing software solutions for the prevention of intrusions into information and telecommunications networks, based on public licenses. The characteristics of the main methods for detecting attacks (intrusions) are given. There is identified  its main shortcomings: lack of adaptability, persistence and verification, high level of erroneous attacks, those misses of cyber attacks, weak opportunities to identify new attacks, lack of ability to determine the attack in its initial stages, practical lack of identification of the attacker and the purpose of the attack, in real time, a significant load of the system and a weak interpretation of the current situation. Prospective ways of their elimination based on the use of hybrid intelligent intrusion prevention systems based on the methods of knowledge engineering, the mathematical apparatus of fuzzy sets theory and fuzzy inference, as well as methods and technologies for data mining are proposed. Obtained results can be considered as a basis for the implementation of new mechanisms for identifying cybernetic attacks and their application during the implementation of intrusion detection systems of the next generation in order to respond to previously unknown types of cybernetic attacks. This will increase the efficiency and validity of the decisions taken by the security administrator of information and telecommunication systems and networks in real time during the detection and prevention of cybernetic attacks.


Keywords


Сybernetic space, information and telecommunication network, cybernetic security, cybernetic attack, intrusion prevention system, fuzzy sets, data mining.

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ISSN 2411-1031 (Print), ISSN 2518-1033 (Online)