CC BY
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геодезического обеспечения Российской Федерации//Интерэкспо Гео-Сибирь. 2016. №Без номера. С.90-106
10. Мусабаев Т. Решая стратегические задачи//Космические исследования и технологии. - Алматы. 2013. №1. С.2-7.
11. Сулейменова А.Х., Ырыскелди Н.Г., Ергалиев Д.С., Ибилдаев Б.К., Мерили Н.А.Программное обеспечение для предварительного расчета системы энергоснабжения космического аппарата дистанционного зондирования Земли (KAZSAT-3). Надежность и качество. Труды международного симпозиума. г.Пенза, РФ - 23 -31 мая 2016 г., №2, С. 235-237.
12. Тулегулов А.Д., Д.С., Мусагулова Ж.С., Нысанбаева А.Б.Геометрические искажения и геометрическая коррекция спутниковых снимков. Надежность и качество-2 013: Международный симпозиум.- Пенза, 2013., том 1. - С.359-361
UDC 004.896
Moldamurat K., Kalmanova D., YergaliyevD., Beybithan T.
Eurasian National University named after. L.N. Gumilyova, Astana, Kazakhstan
INTELLIGENT MECHANISM OF HINDING CRYPTOGRAPHICALLY PROTECTED COMMUNICATION CHANNEL
В статье обсуждаются подходы и методы использования искусственного интеллекта при взломе криптографически защищенного канала связи. В настоящее время данная проблема является весьма актуальной. Весьма вероятно, что в дальнейших исследованиях будут раскрыты новые способы использования методов искусственного интеллекта в области информационной безопасности. Ключевые слова:
ИНФОРМАЦИОННЫЕ СИСТЕМЫ, ИНФОРМАЦИОННО-КОММУНИКАТИВНЫЕ СИСТЕМЫ, ЗАЩИТА ИНФОРМАЦИИ, ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ, МЕТОДЫ, КРИПТОГРАФИЧЕСКИ ЗАЩИЩЕННОГО КАНАЛ СВЯЗИ
But this undoubted convenience in the work becomes very controversial if one takes into account the probability of an information attack, which should be understood as any unauthorized impact on the ICS. Interference-free codes allow to build complex protection systems that simultaneously solve both the problem of protection against interference and the task of protecting against unauthorized exposure.
This feature of noise-immune codes can find its application, for example, in the task of protecting information in digital communication channels from technical leakage - unauthorized reading of transmitted data [1].
Under these conditions, information resources are of enormous material value, and unauthorized access to them, if they are not sufficiently
protected, can lead to global catastrophes or, in the conditions of competition between corporations, firms and entire states, can radically change the situation in favor of those who received such access.
In recent years, research has been actively conducted in the field of constructing methods for protecting information using the theory of cryptography and noise-immune coding [2], and these systems are most actively exposed to computer attacks. Figure 1 shows in general the mechanism of hacking a cryptographic protected public-key communication channel - the most secure system at present. This process is greatly simplified if the cryptanalyst intercepted several crypto- texts sent by subscriber A to subscriber B [3].
Figure 1 - Mechanism of hacking a cryptographic protected communication channel from covered key
Traditionally, existing information security systems do not have the ability to self-learn and use only certain rules embedded in them software or hardware. Creation of perspective information protection systems has been recently identified using intellectual means, such as: expert systems, fuzzy logic systems, neural networks, genetic algorithms.
These approaches realize the evolutionary properties of adaptation, self-organization, training, the possibility of inheritance and the representation of the expertise of information security experts in the form of an unclear If-Then rules system. The increased occurrence of unwanted (malicious) software using new vulnerabilities increased the requirements for modern information security systems and led to the use of artificial intelligence systems [4].
Intellectual resources are actively used to solve problems of information security. Classification and clustering are the main tasks
solved by intellectual means of providing information security (IS) of telecommunication channels in the context of space communications, since continuous monitoring of the system vulnerabilities and the field of threats to the channels is needed [5, 6].
To effectively protect information, it is necessary to use computationally complex cryptographic asymmetric methods of protection.
From the point of view of using hardware implementations of noise-immune codes, symmetric cryptosystems are of interest, since they can be built on the basis of noise-resistant codecs existing in communication media. Note that asymmetric code cryptosystems can not be built on the basis of existing codecs, since in this case part of the secret key - codec - is well known, which significantly reduces the stability of cryptosystems. In the emerging new antivirus utilities, information security analysis pro-
grams, firewalls, there is a tendency to increase the scale of the use of artificial intelligence technologies.
This is facilitated by the availability of training opportunities in them, active development of the methodology of artificial intelligence, increasing the number and complexity of threats to information security. The undoubted advantages of these systems are that they can automatically acquire knowledge in the learning process and have the ability to generalize. In the case of a successful solution to an abnormal
task, the system consolidates the acquired experience in the form of creating a new block of rules for the types of threats and protection algorithms. With each subsequent solution of the same problem, the Intelligent Information Protection System (SISI) has already dealt with the standard task for which there is a ready plan for activating the protection. An architecture of such protection is shown in Figure 2. An asymmetric cryptographic protection system has a complex information transfer system and the security analysis of which can be assigned to an intelligent system.
Figure 2 - Information security scheme with an asymmetric cryptosystem
A necessary element of the security system is a regular analysis of its security, which is performed by the system analysis block. Depending on the quality of the test performed, scanning or probing the system can be carried out. Scanning is a passive analysis that detects the presence of vulnerabilities without taking attacking actions; probing - the method of active analysis, in which an imitation of an attack on the analyzed system is carried out [9].
Probing often occurs after scanning and can use the graph of attacks built upon scanning [7,8].
In the case of unsuccessful attempts to solve an abnormal task, the evolutionary SISI system investigates the causes of failure and organizes the creation of new and destruction of old base elements, algorithms and protection rules, generation of new keys, changes in the length of keys and blocks. These measures protect the confidentiality of the transmission at the expense
of speed. This mode can be considered a transmission by an emergency protocol and a temporary solution.
The most commonly used encryption algorithms are the algorithms RSA, AES, DES, GOST 28147-89 and their hardware implementation on the FPGA in a block of rules and algorithms will speed up the processing of information. Conclusions. A modern trend is the focus on the integration of protective equipment of various levels using artificial intelligence tools. Experience shows that guaranteed protection against unauthorized access to information in open telecommunications systems can be ensured only with an exhaustive threat analysis. And it is possible to realize this most efficiently only with the help of regular scanning of the channel by intelligent systems both at the source and at the message receiver for subsequent active cryptographic protection [9].
LITERATURE
1. VM Sidelnikov. Theory of coding. - Moscow: Fizmatlit, 2008. - C-324 p.
2. Gabidulin EM, Obernikhin VA Codes in the Vandermonde metric and their application // Problems of Information Transmission. - 2003. - P. 39, No. 2. - C. 3-14.
3. Asymmetric cryptosystem with passive interceptor, Labankova E. https://en.wikipedia.org/wiki8
4. Luger DF Artificial intelligence, strategies and methods for solving complex problems [Text]
- 4 th ed. - Williams, 2003. - 864 p.
5. Russell S., Norvig P. Artificial intelligence: a modern approach. M.: Williams, 2007. p.1408.
6. Gorodetski V. I., Kotenko I. V., Karsaev O. Multi-agent technologies for computer network security: Attack simulation, intrusion detection and intrusion detection learning. International Journal of Computer Systems Science & Engineering. - 2003. - No. 4. p. 191-200.
7. Kotenko IV, Ulanov A. Co-operative work of the teams of agents in the protection against network attacks of violation of availability [Electronic resource]. Access mode: www.comsec.spb.ru.
8. Kolegov DN Problems of synthesis and analysis of attack graphs [Electronic resource]. Access mode: http://www.securitylab.ru/contest/2 9 98 68.php■
9. Moldamurat Kh.., // INTELLECTUAL CRYPTOGRAPHIC PROTECTION OF COMMUNICATION CHANNELS // BULLETIN OF SCIENCE of Kostanay Social and Technical University named after academician Zulkharnai Aldamzhar-2 015.-№1 p.111-115.
10. Moldamurat K., Yergaliyev D., Moldamurat A., Tulkibay S. RADAR REMOTE-SENSING INSTRUMENTS OF THE EARTH. Надежность и качество. Труды международного симпозиума. г.Пенза, РФ - 22 -31 мая 2017 г., №2, С. 30-32
11. Moldamurat K., Yergaliyev D., Moldamurat A., Moldan A. USE OF THE MODE-RN MICROCONTRO-LLERS IN RADIO ENGINEERING DEVICES. Надежность и качество. Труды международного симпозиума. г.Пенза, РФ
- 22 -31 мая 2017 г., №2, С. 32-34.
