Software implementation of a DLP-system module for monitoring and controlling corporate network traffic using machine learning

• Main Authors: Anton A. Nedogarok, Nikolai V. Fedorov, Vitaly S. Shvychkov, Maxim I. Kalayda
• Format: Article
• Language: English
• Published: Moscow Engineering Physics Institute 2020-03-01
• Series: Bezopasnostʹ Informacionnyh Tehnologij
• Subjects: dlp-system, machine learning, neural network, confidential information, text classification, dataset.
• Online Access: https://bit.mephi.ru/index.php/bit/article/view/1252
• ISSN: 2074-7128; 2074-7136

The subject of this work is a neural network, the parameters of its architecture and the data array (dataset) for its training. The aim of the work is the software implementation of part of the DLP-system (Data Leak Prevention), which allows to monitor the traffic of a corporate network and to control the transfer of confidential data over this network using a neural network. The entire development process is represented by five stages: theory, design, preparation of data for training the neural network, training the neural network and testing the implemented system. There is a brief overview of the market for such solutions in the article. The parameters used to construct the neural network architecture used to solve the problem of text data classification are described in detail. The result of the work is a functioning part of the DLP system, which allows monitoring the traffic of a corporate network via a web-interface and controlling the transfer of confidential data over this network using a one-dimensional convolutional neural network 1D CNN.