Elaborative Investigation of Blockchain Technology in Intelligent Networks

The fifth generation (5G) network advancements focus to help mixed upright applications by associating heterogeneous gadgets and machines with extreme upgrades regarding high quality of administration, extended organization limit, and improved framework throughput regardless of significant difficulties like decentralization, straightforwardness, dangers of information interoperability, network protection, and security weaknesses. The challenges and limitations of other intelligent 5G intelligent internet of networks (5G IoTs) are also to be met by using blockchain technology with the integration of cloud computing and edge computing technologies. In this chapter, the authors render an elaborated analytics of the empowering of blockchain technology in intelligent networks that includes 5G networks and 5G-based IoT. The solutions for the spectrum management, data sharing, security, and privacy in 5G networks will also be analyzed. It is believed that the chapter would be useful for researchers in the field of blockchain in intelligent networks.

An Intrusion Detection System for Network Security Using Recurrent Neural Network

To maintain the security of vulnerable network is the most essential thing in network system; for network protection or to eliminate unauthorized access of internal as well as external connections, various architectures have been suggested. Various existing approaches has developed different approaches to detect suspicious attacks on victimized machines; nevertheless, an external user develops malicious behaviour and gains unauthorized access to victim machines via such a behaviour framework, referred to as malicious activity or Intruder. A variety of supervised machine algorithms and soft computing algorithms have been developed to distinguish events in real-time as well as synthetic network log data. On the benchmark data set, the NLSKDD most commonly used data set to identify the Intruder. In this paper, we suggest using machine learning algorithms to identify intruders. A signature detection and anomaly detection are two related techniques that have been suggested. In the experimental study, the Recurrent Neural Network (RNN) algorithm is demonstrated with different data sets, and the system’s output is demonstrated in a real-time network context.

Increment of Fault Current due to integration of Distributed Generation and its impact on Distribution Network Protection

BackgroundOne of the new technologies in generating power near the distribution system is called distributed generation which has supportive and destructive characteristics to the power system protection. One of the destructive characteristics of distributed generation is increasing the level of fault current to the protective equipment of the power system. In addition to increment of fault, it also alters the radial nature of the power distribution system and cause the power bidirectional rather than unidirectional. Integration of distributed generation to the distribution network causes increment of fault current effect, reliability drop, and affects security of protection system. The level of failure of protection be contingent on type, size, location and number of distributed generation. This fault current can cause a great damage to the electrical equipment with the miss operations of protective devices. The main aim of this paper is analysis of the fault current level to the protection of distribution network due to the integration of distributed generation which concerns on solar distributed generation, wind distributed generation and combination of solar and wind distributed generations at a time. This paper conducts the analysis for the increment of fault current by the integration of distributed generation and its impact on distribution network protection. ResultsThe analysis and the modeling are conducted on the 15KV distribution network of the radial feeder in Debre Markos town. This paper has covered the ling to ground, line to line and three phase fault analysis and their impact on the protection of distribution system for the wind and solar distributed generation types. After the integration of the distributed generation the fault current is increased by 0.529KA for three phase, 0.74KA for line to ground, 0.467KA for line to line and 0.523KA for line to line to ground. ConclusionsThis paper confirms designing distribution network without forecasting the future demand of electric power users give the protection equipment additional requirement. As the result, the fault current after the integration of distributed generation to the distribution network have great value in terms of power system protection.

National Research Computer Network of Russia as an Infrastructure and Service Platform for a Common Digital Space of Scientific Knowledge

The contribution provides general information on the architecture of the National Research Computer Network (NIKS), operated and developed by JSCC RAS on the assignment of the Ministry of Science and Higher Education of the Russian Federation since 2019. The structure of the service platform of NIKS in the context of the current state and development prospects within the framework of the project of comprehensive network modernization for 2021–2024 is presented. The opportunities provided by NIKS for enhancement of a common digital space of scientific knowledge of the country, increasing the level of accessibility, information security and network protection of its resources and services are denoted. Individual scientific services, information technology services and services for collective use functioning on the basis of the NIKS infrastructure, built, including on massively exploited technologies of federated authentication and developed in cooperation with the global research and education community are introduced and discussed. Special attention is paid to the national identity federation of Russia in the field of science and education, implemented and promising projects and services under its umbrella.

GRADIENT BOOSTING ALGORITHM FOR EARLY DETECTION OF UNKNOWN INTERNET OF THINGS DEVICES

The pervasive availability of the Internet of Things (IoT) markets lures targets for cyber-attacks since most manufactured IoT devices are usually resource-constrained devices. The first powerful line of IoT network protection from these vulnerabilities is detecting IoT devices especially the unauthorized ones by utilizing machine learning (ML) algorithms. Actually, it is so difficult or even impossible to find individual unknown IoT devices during the setup phase but, knowing their manufacturers is a matter to be deliberate. In this paper, a new method based fingerprints generation is introduced to detect the connected devices in the setup phase. Fingerprints for 21 different IoT devices are generated using devices’ network traffic. The whole produced fingerprints of devices are divided into four groups according to their manufacturers or fingerprints similarity proportion. Gradient Boosting Algorithm is applied to achieve the identified purposes. The proposed method is considered as a preparatory study for early detection of unauthorized. The performance evaluation for the proposed method was calculated based on two metrics: Identification accuracy and F1-score. The average identification accuracy rate was around 98.65%, while the average F1-score was about 99%.

MATHEMATICAL MODEL OF PROTECTION IN AN INFORMATION SYSTEM OF A HIERARCHICAL STRUCTURE

С развитием информационных технологий и дальнейшего внедрения их в автоматизированные системы встает вопрос защиты информации и безопасности процессов в сетях. Моделирование компьютерных атак является одним из важных этапов проектирования СЗИ, поскольку дает возможность специалисту по защите информации получить аргументированные доводы о наличии потенциальных угроз безопасности информации на конкретном объекте защиты. Математические модели позволяют провести исследование характеристик определенной атаки для того, чтобы установить, какие средства защиты могут использоваться для её нейтрализации. Цель статьи - построить математическую модель развития вирусной атаки в информационной системе, в которой локальные вычислительные сети иерархически распределены, при этом горизонтальные взаимоотношения между локальными сегментами отсутствуют, а вертикальные – усилены. Данным свойствам обладают хранилища данных федерального и регионального уровней. Модель формализована как система нелинейных дифференциальных уравнений. С помощью построенной модели рассмотрен пример для трехуровневой иерархической системы. Приведены результаты численных экспериментов, которые показывают, что стратегия, при которой большинство ресурсов защиты сети направленные на профилактические мероприятия, дает наилучший результат. Модель может найти применение при создании методологии математического и компьютерного моделирования управления комплексной безопасностью многоуровневых иерархических систем, а также для обеспечения принятия управленческих решений в сфере защиты информации. Today, there is a question of information protection and process security in networks, as information technologies are actively being introduced into automated systems. Modeling of computer attacks is one of the important stages of designing an information security system. E allows an information security specialist to receive reasoned arguments about the presence of potential threats to the security of information at a specific object of protection. Mathematical models allow us to conduct a study of the characteristics of cyberattack and determine which means of protection can be used to neutralize it. The purpose of the article is to build a mathematical model of the development of a virus attack in an information system of a hierarchical structure. There are no horizontal relationships between local segments, and vertical ones are strengthened. Data warehouses at the federal and regional levels have these properties. The model is formalized as a system of nonlinear differential equations. An example for a three-level hierarchical system is considered. The results of numerical experiments are presented. They showed that the strategy in which the majority of network protection resources are directed to preventive measures gives the best result. The model can be used to create a methodology for mathematical and computer modeling of complex security management of multi-level hierarchical systems, as well as to ensure managerial decision-making in the field of information security