The Fifteenth International Conference on Condition Monitoring and Machinery Failure Prevention Technologies
CM2018 / MFPT2018
Monday 10 to Wednesday 12 September 2018
East Midlands Conference Centre and Orchard Hotel, Nottingham, UK
ISBN 978 0 903132 70 2
[129] Monitoring the dangerous activity of computer network users
Kuravsky L.S.*, Yuryev G.A.*, Scribtsov P.V.#, Chervonenkis M.A.#, Konstantinovsky A.A.*, Shevchenko A.A.*, Isakov S.S.*
*Moscow State University of Psychology and Education,
Computer Science Faculty,
#"Pavlin Techno” company
Two approaches to monitoring the dangerous activity of computer network users are presented. The first one relies on the technique of statistical hypotheses testing and uses self-organizing feature maps (Kohonen networks) for generating target statistics. The second approach recognizes dangerous activity via executed sequences of relevant typical actions, with their dynamics being represented with the aid of Markov chains.
Acknowledgement
The work was financially supported by the Ministry of Education and Science of the Russian Federation within the framework of the grant agreement dated September 26, 2017 No. 14.579.21.0155 (Unique identifier of the agreement RFMEFI57917X0155) for the implementation of applied research and experimental development on the topic: "Development of intelligent algorithms detection of network threats in the cloud computing environment and methods of protection against them, based on the analysis of traffic dynamics and determination of deviations in user behavior”
http://www.bindt.org/events/CM2018-and-MFPT2018/programme-10-september-2018/
ISBN 978 0 903132 70 2
[104] Dimensionality reduction with aid of stochastic swarm clusterisation method
Grigory A Yuryev, Ekaterina K Verkhovskaya and Nataliya E Yuryeva
Moscow State University of Psychology and Education
Moscow, Russia
Consider a non-linear dimensionality reduction method which takes into account the discriminating power of the solution found for given values of the categorical variable associated with each observation. Stochastic optimization method known as the "Particle swarm optimization" is proposed to found characteristics that ensure the best separation of observations in terms of a given quality functional. The basis for evaluating the quality of the solution lies in the purity of the clusters obtained with the k-means method, or with using self-organizing Kohonen feature maps.
Acknowledgements
"This work was financially supported by the Ministry of Education and Science of the Russian Federation within the framework of the Subsidy Agreement dating September 26, 2017 No. 14.576.21.0092 (Unique identifier of the agreement RFMEFI57617X0092) for the implementation of applied scientific research on the topic: "Development of a neural network forecasting system for aviation incidents and safety risk management based on historical data including many parameters and text descriptions of events".
http://www.bindt.org/events/CM2018-and-MFPT2018/programme-10-september-2018/
ISBN 978 0 903132 70 2
[113] Convolutional feature extraction from human computer interaction data in behavioural biometric
Grigory A Yuryev
Moscow State University of Psychology and Education
Moscow, Russia
The paper considers methodology of user biometric profile formation from human computer interaction (HCI) data by applying a discrete-time convolution to set of measured user activity parameters. Presented technology was successfully verified in pilot study aimed at determining effective approaches to the user behavior analysis problems. Provided detailed experiment design description and formal specification of initial parameters used for building up behavioral profiles. Obtained results allow to conclude that certain parameters emerging in typical HCI scenarios could be used to provide a mechanism for the “continuous” user authentication procedure after standard authorization was performed.
Acknowledgement
The work was financially supported by the Ministry of Education and Science of the Russian Federation within the framework of the grant agreement dated September 26, 2017 No. 14.579.21.0155 (Unique identifier of the agreement RFMEFI57917X0155) for the implementation of applied research and experimental development on the topic: "Development of intelligent algorithms detection of network threats in the cloud computing environment and methods of protection against them, based on the analysis of traffic dynamics and determination of deviations in user behavior”
http://www.bindt.org/events/CM2018-and-MFPT2018/programme-10-september-2018/
ISBN 978 0 903132 70 2
[126] Forecasting aviation accidents with the aid of probabilistic models
P.N. Dumin and D.A. Pominov
Moscow State University of Psychology and Education
Moscow, Russia
A probabilistic model for forecasting aviation accidents on the basis of historical data is under consideration. Discrete-states continuous-time Markov processes are applied to obtain probabilistic distributions of accidents for given time periods. A numerical algorithm for identifying model parameters is presented. The results obtained can be used by airlines to plan their activities.
Acknowledgements
"This work was financially supported by the Ministry of Education and Science of the Russian Federation within the framework of the Subsidy Agreement dating September 26, 2017 No. 14.576.21.0092 (Unique identifier of the agreement RFMEFI57617X0092) for the implementation of applied scientific research on the topic: "Development of a neural network forecasting system for aviation incidents and safety risk management based on historical data including many parameters and text descriptions of events".
http://www.bindt.org/events/CM2018-and-MFPT2018/programme-10-september-2018/