Методика обнаружения аномалий и кибератак на основе интеграции методов фрактального анализа и машинного обучения

В современных сетях передачи данных для постоянного мониторинга сетевого трафика и обнаружения в нем аномальной активности, а также идентификации и классификации кибератак, необходимо учитывать большое число факторов и параметров, включая возможные сетевые маршруты, времена задержки данных, потери пакетов и новые свойства трафика, отличающиеся от нормальных. Все это является побудительным мотивом к поиску новых методов и методик обнаружения кибератак и защиты от них сетей передачи данных. В статье рассматривается методика обнаружения аномалий и кибератак, предназначенная для использования в современных сетях передачи данных, которая основывается на интеграции методов фрактального анализа и машинного обучения. Методика ориентирована на выполнение в реальном или близком к реальному масштабе времени и включает несколько этапов: (1) выявления аномалий в сетевом трафике, (2) идентификации в аномалиях кибератак и (3) классификации кибератак. Первый этап реализуется с помощью методов фрактального анализа (оценки самоподобия сетевого трафика), второй и третий – с применением методов машинного обучения, использующих ячейки рекуррентных нейронных сетей с долгой краткосрочной памятью. Рассматриваются вопросы программной реализации предлагаемой методики, включая формирование набора данных, содержащего сетевые пакеты, циркулирующие в сети передачи данных. Представлены результаты экспериментальной оценки предложенной методики, полученные с использованием сформированного набора данных. Результаты экспериментов показали достаточно высокую эффективность предложенной методики и разработанных для нее решений, позволяющих осуществлять раннее обнаружение как известных, так и неизвестных кибератак

Anemia and quality of life of chronic kidney disease patients on renal replacement therapy by programmed hemodialysis

Aim. To study relationship between anemia and health-related quality of life (HRQOL) of chronic kidney disease stage 5 patients (CKD) undergoing renal replacement therapy by programmed hemodialysis. Materials and Methods. The study was conducted on the basis of dialysis centers/departments in Moscow. The Russian-language version of the KDQOL-SFTM questionnaire, validated in Russia, was used to assess the HRQOL of patients on hemodialysis. A total of 723 patients were interviewed. Hemoglobin (Hb) levels were assessed in 442 patients based on outpatient records. To identify the relationship between Hb level and HRQOL scales, a correlation analysis was performed, as well as a HRQOL analysis in the three groups differing in hemoglobin levels (100; 100-120 and 120 g/l). Statistical data processing was carried by SPSS.22 and using parametric and nonparametric statistical methods. Results. Correlation relationship was revealed between Hb and HRQOL of patients on hemodialysis on the scales "symptoms/problems", "pain", "vital activity, energy" and "total physical component of health". According to the scales "symptoms/problems" and "vital activity, energy", higher rates were noted among patients with Hb higher than the target level, which confirms the recommendations concerning the possibility of achieving higher Hb among individual patients who have not serious concomitant diseases and cardiovascular complications in order to improve their quality of life. Conclusion. HRQOL assessment is important tool for planning and evaluating the effectiveness of anemia drug therapy among patients with CKD on hemodialysis

Ensuring SDN Resilience under the Influence of Cyber Attacks: Combining Methods of Topological Transformation of Stochastic Networks, Markov Processes, and Neural Networks

The article proposes an approach to ensuring the functioning of Software-Defined Networks (SDN) in cyber attack conditions based on the analytical modeling of cyber attacks using the method of topological transformation of stochastic networks. Unlike other well-known approaches, the proposed approach combines the SDN resilience assessment based on analytical modeling and the SDN state monitoring based on a neural network. The mathematical foundations of this assessment are considered, which make it possible to calculate the resilience indicators of SDN using analytical expressions. As the main indicator, it is proposed to use the correct operation coefficient for the resilience of SDN. The approach under consideration involves the development of verbal models of cyber attacks, followed by the construction of their analytical models. In order to build analytical models of cyber attacks, the method of topological transformation of stochastic networks (TTSN) is used. To obtain initial data in the simulation, the SDN simulation bench was justified and deployed in the EVE-NG (Emulated Virtual Environment Next Generation) virtual environment. The result of the simulation is the time distribution function and the average time for the cyber attack implementation. These results are then used to evaluate the SDN resilience indicators, which are found by using the Markov processes theory. In order to ensure the resilience of the SDN functioning, the article substantiates an algorithm for monitoring the state of controllers and their automatic restructuring, built on the basis of a neural network. When one is choosing a neural network, a comparative evaluation of the convolutional neural network and the LSTM neural network is carried out. The experimental results of analytical modeling and simulation are presented and their comparative evaluation is carried out, which showed that the proposed approach has a sufficiently high accuracy, completeness of the obtained solutions and it took a short time to obtain the result

Methodology for Management of the Protection System of Smart Power Supply Networks in the Context of Cyberattacks

This paper examines an approach that allows one to build an efficient system for protecting the information resources of smart power supply networks from cyberattacks based on the use of graph models and artificial neural networks. The possibility of a joint application of graphs, describing the features for the functioning of the protection system of smart power supply networks, and artificial neural in order to predict and detect cyberattacks is considered. The novelty of the obtained results lies in the fact that, on the basis of experimental studies, a methodology for managing the protection system of smart power supply networks in conditions of cyberattacks is substantiated. It is based on the specification of the protection system by using flat graphs and implementing a neural network with long short-term memory, which makes it possible to predict with a high degree of accuracy and fairly quickly the impact of cyberattacks. The issues of software implementation of the proposed approach are considered. The experimental results obtained using the generated dataset confirm the efficiency of the developed methodology. It is shown that the proposed methodology demonstrates up to a 30% gain in time for detecting cyberattacks in comparison with known solutions. As a result, the survivability of the Self-monitoring, Analysis and Reporting technology (SMART) grid (SG) fragment under consideration increased from 0.62 to 0.95

Isomer ratios for products of photonuclear reactions on Rh

Over the past several years various preequilibrium models for nuclear reaction mechanisms description were developed. Diversified detailed experimental data in the medium excitation energy region for nuclei are needed for reasonable selection among these theoretical models. Lack of experimental data in this energy region does essentially limit the possibilities for analysis and comparison of different preequilibrium theoretical models. For photonuclear reactions this energy range covers 30-100 MeV. Experimental measurements and estimations of isomer ratios for products of photonuclear reactions with multiple particle escape on antimony were performed using bremsstrahlung spectrum as projectile with end-point energies 74,9 and 85,7 MeV. Method of the induced activity measurement was applied. For acquisition of gamma spectra we used HPGe spectrometer with 20% relative efficiency. Linear accelerator of electrons LU-40 was a source of bremsstrahlung. Energy resolution of electron beam was about 1% and a mean electron current varied within (3.8 - 5.3)μA

Isomer ratios for products of photonuclear reactions on Rh

Over the past several years various preequilibrium models for nuclear reaction mechanisms description were developed. Diversified detailed experimental data in the medium excitation energy region for nuclei are needed for reasonable selection among these theoretical models. Lack of experimental data in this energy region does essentially limit the possibilities for analysis and comparison of different preequilibrium theoretical models. For photonuclear reactions this energy range covers 30-100 MeV. Experimental measurements and estimations of isomer ratios for products of photonuclear reactions with multiple particle escape on antimony were performed using bremsstrahlung spectrum as projectile with end-point energies 74,9 and 85,7 MeV. Method of the induced activity measurement was applied. For acquisition of gamma spectra we used HPGe spectrometer with 20% relative efficiency. Linear accelerator of electrons LU-40 was a source of bremsstrahlung. Energy resolution of electron beam was about 1% and a mean electron current varied within (3.8 - 5.3)μA

Analyzing the Clinical Potential of Stromal Vascular Fraction: A Comprehensive Literature Review

Background: Regenerative medicine is evolving with discoveries like the stromal vascular fraction (SVF), a diverse cell group from adipose tissue with therapeutic promise. Originating from fat cell metabolism studies in the 1960s, SVF’s versatility was recognized after demonstrating multipotency. Comprising of cells like pericytes, smooth muscle cells, and, notably, adipose-derived stem cells (ADSCs), SVF offers tissue regeneration and repair through the differentiation and secretion of growth factors. Its therapeutic efficacy is due to these cells’ synergistic action, prompting extensive research. Methods: This review analyzed the relevant literature on SVF, covering its composition, action mechanisms, clinical applications, and future directions. An extensive literature search from January 2018 to June 2023 was conducted across databases like PubMed, Embase, etc., using specific keywords. Results: The systematic literature search yielded a total of 473 articles. Sixteen articles met the inclusion criteria and were included in the review. This rigorous methodology provides a framework for a thorough and systematic analysis of the existing literature on SVF, offering robust insights into the potential of this important cell population in regenerative medicine. Conclusions: Our review reveals the potential of SVF, a heterogeneous cell mixture, as a powerful tool in regenerative medicine. SVF has demonstrated therapeutic efficacy and safety across disciplines, improving pain, tissue regeneration, graft survival, and wound healing while exhibiting immunomodulatory and anti-inflammatory properties

Comparative Analysis of Stromal Vascular Fraction and Alternative Mechanisms in Bone Fracture Stimulation to Bridge the Gap between Nature and Technological Advancement: A Systematic Review

Background: Various stimulation methods, including electrical, ultrasound, mechanical, and biological interventions, are explored, each leveraging intricate cellular and molecular dynamics to expedite healing. The advent of stromal vascular fraction (SVF) marks a significant stride, offering multifarious benefits in bone healing, from enhanced bone formation to optimal vascular integration, drawing a harmonious balance between innate mechanisms and scientific advancements. Methods: This systematic review was conducted focusing on literature from 2016 to 2023 and encompassing various bone healing stimulation mechanisms like SVF, electrical, ultrasound, and mechanical stimulation. The extracted data underwent meticulous synthesis and analysis, emphasizing comparative evaluations of mechanisms, applications, and outcomes of each intervention. Results: The reviewed studies reveal the potential of SVF in bone fracture healing, with its regenerative and anti-inflammatory effects. The purification of SVF is crucial for safe therapeutic use. Characterization involves flow cytometry and microscopy. Studies show SVF’s efficacy in bone regeneration, versatility in various contexts, and potential for clinical use. SVF appears superior to electrical, ultrasound, and mechanical stimulation, with low complications. Conclusions: This review compares bone healing methods, including SVF. It provides valuable insights into SVF’s potential for bone regeneration. However, due to limited human studies and potential bias, cautious interpretation is necessary. Further research is essential to validate these findings and determine the optimal SVF applications in bone healing