Browsing by Author "Ostapets, Denis O."

Now showing 1 - 2 of 2

Detection of Attacks on a Computer Network Based on the Use of Neural Networks Complex
(Дніпровський національний університет залізничного транспорту імені академіка В. Лазаряна, Дніпро, 2020) Zhukovyts’kyy, Igor V.; Pakhomova, Victoria M.; Ostapets, Denis O.; Tsyhanok, O. I.
ENG: Purpose. The article is aimed at the development of a methodology for detecting attacks on a computer network. To achieve this goal the following tasks were solved: to develop a methodology for detecting attacks on a computer network based on an ensemble of neural networks using normalized data from the open KDD Cup 99 database; when performing machine training to identify the optimal parameters of the neural network which will provide a sufficiently high level of reliability of detection of intrusions into the computer network. Methodology. As an architectural solution of the attack detection module, a two-level network system is proposed, based on an ensemble of five neural networks of the multilayer perceptron type. The first neural network to determine the category of attack class (DoS, R2L, U2R, Probe) or the fact that there was no attack; other neural networks – to detect the type of attack, if any (each of these four neural networks corresponds to one class of attack and is able to identify types that belong only to this class). Findings. The created software model was used to study the parameters of the neural network configuration 41–1–132–5, which determines the category of the attack class on the computer network. It is determined that the optimal training speed is 0.001. The ADAM algorithm proved to be the best for optimization. The ReLU function is the most suitable activation function for the hidden layer, and the hyperbolic tangent function – for the output layer activation function. Accuracy in test and validation samples was 92.86 % and 91.03 %, respectively. Originality. The developed software model, which uses the Python 3.5 programming lan-guage, the integrated development environment PyCharm 2016.3 and the Tensorflow 1.2 framework, makes it pos-sible to detect all types of attacks of DoS, U2R, R2L, Probe classes. Practical value. Graphical dependencies of accuracy of neural networks at various parameters are received: speed of training; activation function; optimization algorithm. The optimal parameters of neural networks have been determined, which will ensure a sufficiently high level of reliability of intrusion detection into a computer network.
Software–Hardware Random Numbers Generating Complex Based on Mobile Device
(CEUR-WS Team, Aachen, Germany, 2025) Shynkarenko, Viktor I.; Ostapets, Denis O.; Opriatnyi, Artur
ENG: The effectiveness of many modern technologies in such fields as statistics analysis, computer games, gambling, testing, computer graphics, simulation, cryptography, information security algorithms and any others depends on the quality of random number generating means. The article represents the development principles of the software – hardware pseudo and true random numbers generating complex based on using a mobile device. An analysis of entropy sources provided by mobile devices is submitted. The accelerometer, gyroscope, and magnetometer sensors of mobile device are chosen as entropy sources. The complex architecture and the TCP - connection based communication protocol between the complex parts have been developed. The total number of considered modes for generating pseudo and true random number sequences is six. The software of server, client and mobile parts of the complex is implemented. Research of pseudo and true random number sequences quality obtained in different modes using a statistical and visual tests suite is submitted. An analysis of the results and a comparison with the previously known ones is performed. Recommendations for using different generating modes are given.