Персона: Финошин, Михаил Александрович
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Институт интеллектуальных кибернетических систем
Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.
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- ПубликацияТолько метаданныеTiming covert channels detection cases via machine learning(2019) Epishkina, A.; Finoshin, M.; Kogos, K.; Yazykova, A.; Епишкина, Анна Васильевна; Финошин, Михаил Александрович; Когос, Константин Григорьевич© 2019 IEEE.Currently, packet data networks are widespread. Their architectural features allow constructing covert channels that are able to transmit covert data under the conditions of using standard protection measures. However, encryption or packets length normalization, leave the possibility for an intruder to transfer covert data via timing covert channels (TCCs). In turn, inter-packet delay (IPD) normalization leads to reducing communication channel capacity. Detection is an alternative countermeasure. At the present time, detection methods based on machine learning are widely studied. The complexity of TCCs detection based on machine learning depends on the availability of traffic samples, and on the possibility of an intruder to change covert channels parameters. In the current work, we explore the cases of TCCs detection via.
- ПубликацияТолько метаданныеInternet Users Authentication via Artificial Intelligence(2020) Kogos, K. G.; Finoshin, M. A.; Gentyuk, V. A.; Когос, Константин Григорьевич; Финошин, Михаил Александрович© 2020, Springer Nature Switzerland AG.The number of Internet users increases and the Internet is part of people’s daily lives, as a result, the behavior of the user becomes free and informal. This is the basis of the assumption that the manner of user actions on the Internet has become a factor that can be used by authentication using artificial intelligence. In turn, existing works related to users’ web browsing behavior-based authentication with using machine learning do not analyze some important behavioral user’s characteristics, such as patterns of behavior or user behavior on a frequently visited resource. It causes to suggest own features and check their contribution to the accuracy of the system. The aim of this work is to study the possibility of introducing a map of clicks, bigrams, trigrams of frequent web pages and their domains, evaluation of the contribution of added features. In this work, we replace the web pages’ genre classification by domain classification and don’t take into account the spikes in views. We have created a system based on artificial intelligence. As a work result, we have shown a significant improvement in the accuracy of the system using the click map and a slight improvement in the use of bigrams and trigrams.
- ПубликацияТолько метаданныеArtificial Intelligence to Detect Timing Covert Channels(2020) Yazykova, A.; Finoshin, M.; Kogos, K.; Финошин, Михаил Александрович; Когос, Константин Григорьевич© 2020, Springer Nature Switzerland AG.The peculiarities of the batch data transmission networks make it possible to use covert channels, which survive under standard protective measures, to perform data leaks. However, storage covert channels can be annihilated by means of limiting the flow capacity, or by use of encryption. The measures against storage covert channels cannot be implemented against timing covert channels (TCCs), otherwise their usage has to be conditioned by certain factors. For instance, while packet encryption an intruder still possesses the ability to covertly transfer the data. At the same time, normalization of inter-packet delays (IPDs) influences the flow capacity in a greater degree than sending fixed-length packets does. Detection can be called an alternative countermeasure. At the present time, detection methods based on artificial intelligence have been widespreadly used, however the possibility to implement these methods under conditions of a covert channel parametrization has not been investigated. In the current work, we study the possibility to implement artificial intelligence for detecting TCCs under conditions of varying covert channel characteristics: flow capacity and encoding scheme. The detection method is based on machine learning algorithms that solve the problem of binary classification.