Перегляд за Автор "Danchuk, Serhii"

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  • Ескіз
    Документ
    Heating optimization system in a smart home based on fuzzy logic and integration with cloud services
    (Хмельницький національний університет, 2025) Lytvinchuk, Ihor; Savenko, Bohdan; Danchuk, Serhii
    Smart home technologies are increasingly being used to automate various aspects of everyday life, and one of the main problems these systems solve is energy optimization. Heating is one of the largest energy consumers in a home, so its efficient management plays a key role in reducing energy costs and increasing the comfort level of residents. A fuzzy logic-based system for optimizing the use of heating in a smart home is an important step towards energy efficiency and comfort in modern residential buildings. The relevance of this work lies in the fact that existing heating systems in Smart Homes are often not fully optimized, especially in terms of fuel management and reducing the frequency of temperature fluctuations. Many current systems do not fully take into account variable conditions such as outdoor temperature, time of day, humidity levels, or individual user needs. This results in inefficient operation: fuel consumption can be excessive and room temperatures fluctuate frequently, creating discomfort for occupants. Frequent changes in temperature can also negatively affect human health, and excessive fuel consumption leads to economic losses and increased environmental impact. Optimization of these processes through the use of fuzzy logic can achieve a more stable and energy-efficient heating system, which is essential for improving comfort and reducing costs. This paper proposes a fuzzy logic-based system for optimizing the use of heating in a Smart home. According to the results obtained, the use of fuzzy logic significantly improves the stability of the temperature in the house, which is important for the comfort of the residents. For the experiments, two models were compared: a basic heating model and a model based on fuzzy logic. The basic system, which does not take into account variable factors with this level of flexibility, leads to large and sharp temperature fluctuations, which can create discomfort and increase energy consumption. Instead, the fuzzy logic model demonstrates smoother and more stable temperature control, which can significantly reduce energy costs.
  • Ескіз
    Документ
    Система збору даних та виявлення розподілених атак відмова в обслуговуванні у мережах на основі протоколу RPL
    (Хмельницький національний університет, 2023) Нічепорук, Андрій; Нічепорук, Анастасія; Данчук, Сергій; Коротков, Юрій; Цаволик, Тарас; Nicheporuk, Andrii; Nicheporuk, Anastasiia; Danchuk, Serhii; Korotkov, Yurii; Tsavolyk, Taras
    В роботі представлено систему збору даних та виявлення розподілених атак відмова в обслуговуванні у мережах на основі протоколу RPL. Система складається із трьох модулів: модуль збору даних, модуль класифікації та модуль виявлення. Головною особливістю модуля збору даних було те, що збір даних забезпечувався декількома сніферами, що встановлені у мережі, і з подальшою агрегацією зібраних даних. Для реалізації модуля класифікації проведено дослідження методу опорних векторів та багатошарового персептрона. Модуль виявлення використовувався для трансляції повідомлення про аномальну поведінку на решту вузлів ІоТ мережі, що містять ідентифікатор скопроментованого вузла та шлях до нього. Denial of Service (DoS) or Distributed Denial of Service (DDoS) attacks are among the main security threats to Internet of Things (IoT) networks today. This type of attack leads to the loss of access to the device or the resources it offers. Therefore, with the aim of countering such cyber threats, it is proposed, a system for data collection and detection of distributed denial of service in the RPL-based networks is presented. The system consists of three modules: a data gathering module, a classification module and a detection module. The main purpose of the data collection module is that data collection was provided by several sniffers installed in the RPL network and with subsequent aggregation of the collected data. For the implementation of the classification module, research was carried out on the method of support vector machines (SVM) and a multilayer perceptron (MLP). The detection module was used to broadcast a message about the abnormal behaviour to the rest of the RPL network nodes, containing the ID of the compromised node and the path to it. To evaluate the efficiency of the proposed system that is based on the data collected by the data gathering module, a number of experiments are conducted. To obtain the data set for the experiments, an infrastructure based on the Ubuntu operating system and the Cooja simulator are deployed, which allowed to simulate the RPL network. Based on the operation of the deployed network, network traffic was collected that corresponded to both legitimate traffic and traffic during a black hole attack. The total number of test data was 24,023 samples. According to the research results, it is established that the SVM-based model demonstrated better performance level, in particular, the accuracy of detecting denial-of-service attacks was 89.6%, while the rate of false positives was 6%.