Authors

Virtualization of information object vulnerability testing container based on DeX technology and deep learning neural networks

The modern development of information security tools, along with the improvement of remote access methods, allows software and hardware to be audited without the need for direct access to the system under test. One of its components is related to the implementation of software on mobile ARM processor architectures. Within this direction of development, the approach that allows integrating Linux kernel-based distributions by introducing a virtual container chroot (change root) into the Android OS- based system and, consequently, performing penetration testing without the need to use personal computers is highlighted. An example of this approach is the Kali NetHunter distribution which allows remote system administration functionality through the KeX module. Besides the obvious advantages of KeX functionality, some disadvantages should also be mentioned: firstly, the low speed of GUI processing due to translation to remote hosts and the need to support translation at operating system level; secondly, the consumption of energy resources when using the desktop features of the KeX module. In order to solve the mentioned problems, a system of virtualization of energy-efficient container for testing the vulnerabilities of critical information objects has been developed and based on the principle of multi-containerization. The software of the system is represented by two components: an enlarged module for integration of the chroot container into the DeX environment (primary), and an enlarged module for ensuring energy efficiency using predictive neural network models based on variable time intervals (secondary). As a result of comparing the effectiveness of existing and implemented approaches in penetration testing, it is noted that the proposed system can be used in testing the security of particular platforms and systems, including highly sensitive information objects or resources. Read more...

Neural network model to support decision-making on managing cooperative relations in innovative ecosystems

Currently, the specifics of external conditions and peculiarities of innovation activity main subjects development determine not only the need for close, long-term scientific and technical cooperation with the state for the sustainable development of territories, but also the need to develop and substantiate proposals for managing the development of innovation processes in such a system as a whole. The article proposes a model for the representation of scientific and industrial interaction in the implementation of regional innovation processes in the form of a three-dimensional "slice" of the triple helix as a resource VRIO-profile of cooperative formation, which allows to clearly demonstrate the system of relations, identify in which direction the problem area is, influencing which it will be possible to return the system to an equilibrium state of sustainable development in a strategic perspective. The analysis of modern scientific works shows the relevance, necessity and effectiveness of using methods based on neural networks to predict changes in the state of complex socio-economic systems, such as regional innovation systems. Existing approaches, as a rule, demonstrate a narrow focus and belonging to a separate enterprise or organization, and therefore do not meet all the requirements from both the implementation of the innovation process itself and the modification of the external environment. In this connection, the authors proposed an information and analytical solution for using the described model to support decision-making on the management of cooperative formations. The developed program is based on predicting the future state (position in a three-dimensional coordinate system) of the system using deep neural networks, namely recurrent. The described practical approbation of the model can in the future serve as a basis for decision-making on the choice of forms and directions of interaction of cooperative formations in the strategic perspective. Read more...

Development of a secure neural traffic tunneling system with post-performance evaluation

Currently information exchange methods and means of communication development are being done a significant impact on the level of all industrial and economic entities innovation potential, which is also the same for their group formations, such as regional complexes. It is necessary to note high degree of integration and interdependence of all such systems elements and processes closely interconnected by different kind of networks. Among them, it is possible to highlight the interaction between participants of scientific and industrial cluster within the framework of innovative activities, which should provide possibility to transfer and receive various kinds of data, which could be both open and confidential type. At the current stage, there is not many applied tools for ensuring confidentiality in the implementation of these processes. For example, they partially solve the problem of traffic tunnelling systems based on OpenVPN or WireGuard tunnels, and other software solutions provide the potential of an extensible cloud (Nextcloud). However, analysing the functionality of these solutions, it is possible to identify shortcomings that do not allow their implementation in the complex production and economic systems processes of innovative development. Thus, existing traffic tunnelling solutions are not adapted for deployment on a corporate scale with a flexible organisational structure. In solutions based on Nextcloud, the complexity disadvantages of the server configuration and the cost of the primary software configuration are highlighted. To solve the above problems, in article has been proposed an intelligent traffic tunneling system, which is based on using additional means of primary automated OpenVPN connection initialization at neural module expense. A dynamic digital fingerprint distribution system with two-way key exchange was used as an authorization server. The developed software solution was tested and then compared with existing analogues. This experiment may to conclusion that the developed software solution is not inferior in a number of aspects to existing methods, and can subsequently be used to ensure secure information and communication exchange between industrial and economic entities in clusters during innovative processes implementation. Read more...

Analysis and testing of neural network TCP/IP packet routing algorithms in private virtual tunnels

One of the most important components of the global Internet are traffic control and management systems. In order to achieve uninterrupted information and communication interaction, the organization of the process is constantly changing, covering not only individual subnets, but also p2p network architectures. The dominant areas for improving the network structure include 5G, IoT and SDN technologies, but their implementation in practice leaves the issue of ensuring the information security of networks built on their basis without a satisfactory solution. Current virtual tunnel deployment topologies and intelligent traffic distribution components provide only partial solutions, particularly in the form of access control based on user traffic and security through dedicated user certificates. The deployment of a tunnel is of particular importance in cases where it is necessary to ensure consistency and coordination of the work of complex socio-economic systems, an example of which is the information and communication exchange between participants in scientific and industrial clusters formed to implement projects for the creation of innovative products. However, existing solutions have disadvantages such as the need to purchase a license for full-featured access to the software product and specialized configuration of client-server authentication that provides secure access to a remote network route. The approach proposed by the authors, based on neural network distribution of traffic between clients of a private dedicated network, allows us to eliminate the noted shortcomings. Based on this principle, a multi-module system for intelligent packet routing was created and tested through unit testing. An analysis of the effectiveness of using a trained network address distribution model is presented in comparison with the use of a DHCP server based on the isc-dhcp-server package, distributed as the dhcpd service. Read more...