ATTACK DETECTION IN ENTERPRISE NETWORKS BY MACHINE LEARNING METHODS

Detection of network attacks is currently one of the most important problems of secure use of enterprise networks. Network signature-based intrusion detection systems cannot detect new types of attacks. Thus, the urgent task is to quickly classify network traffic to detect network attacks. The article describes algorithms for detecting attacks in enterprise networks based on data analysis that can be collected in them. The UNSW-NB15 data set was used to compare machine learning methods for classifying attack or-normal traffic, as well as to identify nine more popular classes of typical attacks, such as Fuzzers, Analysis, Backdoors, DoS, Exploits, Generic, Reconnaissance, Shellcode and Worms. Balanced accuracy is used as the main metric for assessing the accuracy of the classification. The main advantage of this metric is an adequate assessment of the accuracy of classification algorithms given the strong imbalance in the number of marked records for each class of data set. As a result of the experiment, it was found that the best algorithm for identifying the presence of an attack is RandomForest, to clarify its type - AdaBoost

Project of automated adaptive system for individual support of talented students in the regional information and educational environment

This paper describes the project of an automated adaptive system for individual support of talented students in the regional information and educational environment, which is planned to be implemented in the Orenburg region on the platform of the Orenburg State University. The architecture of the system is proposed, basic roles and functional capabilities of users are described, as well as mechanisms for calculating the absolute and relative ratings of students

DISTRIBUTION OF THE NEURAL NETWORK BETWEEN MOBILE DEVICE AND CLOUD INFRASTRUCTURE SERVICES

Neural networks become the only way to solve problems in some areas. Such tasks as recognition of images, sounds, classification require serious processor power and memory for training and functioning of the network. Modern mobile devices have quite good characteristics for primary layers of deep neural networks, but there are not enough resources for whole network. Since neural networks for mobile devices are trained separately on external resources, a method of distributed work of a neural network with vertical distribution over sets of layers with synchronization of training data was developed. The model is divided after saving its state, all layers on the mobile device are converted to the format for the mobile framework and synchronized with the device after training on a distributed platform. Variables and coefficients are formed separately, which allows to significantly reduce the size of the neural network data file uploaded to the device. An algorithm for automatic selection of a neural network separation point was proposed. It based on the data amount transferred between the layers and the load on the mobile device resources. The approach allows to use full-size deep neural networks with a mobile device. Performance experiment showed possibility of obtains an acceptable response even with an unstable communication channel without overloading communication channels and device resources