Ivan Opirskyy

The object of research is IEEE 802.11 (Wi-Fi) networks, which are often the targets of a group of attacks called "evil twin". Research into this area is extremely important because Wi-Fi technology is a very common method of connecting to a network and is usually the first target of cybercriminals when they attack businesses. With the help of a systematic analysis of the literature focused on countering attacks of the "evil twin" type, this work identifies the main advantages of using artificial intelligence systems in the analysis of network data and identification of intrusions in Wi-Fi networks. To evaluate the effectiveness of intrusion detection and cybercrime analysis, a number of experiments as close as possible to real attacks on Wi-Fi networks were conducted. As part of the research reported in this paper, a method is proposed for detecting cybercrimes in IEEE 802.11 (Wi-Fi) wireless networks using artificial intelligence, namely a model built on the basis of the k-nearest neighbors method. This method is based on the classification of previously collected data, namely the signal strength from the access point, and then continuous comparison of the newly collected data with the trained model. A compact and energy-efficient prototype of a hardware and software system has been designed for the implementation of monitoring, analysis of ethernet network packets and data storage based on time series. In order to reduce the load on the computer network and taking into account the limited computing power of the system, a method of data aggregation was proposed, which ensures fast transfer of information. The results, namely 100 % of test cases (more than 7 thousand), were classified correctly, which indicates that the chosen method of data analysis will significantly increase the security of information and communication systems at the state and private levels

The object of research are decoys with dynamic attributes. This paper discusses the impact of decoys involving blockchain technologies on the state of information security of the organization and the process of researching cybercrime. This is important because most cybercrimes are detected after the attacker gains access to sensitive data. Through systematic analysis of the literature focused on assessing the capabilities of decoy and blockchain technologies, this work identifies the main advantages of decoys that utilize blockchain technology. To assess the effectiveness of attacker detection and cybercrime analysis, controlled experiments were conducted using a blockchain-based decoy system that we developed aimed at determining network performance. As part of the study reported here, a technique is proposed to detect cybercrime using decoys based on blockchain technology. This technique is based on the fact that the attributes of the system change dynamically. Such a technique has made it possible to obtain a system model that solves the task of detecting decoys by intruders. In addition, the developed scheme reduces the load in contrast to the conventional fixed solution. The results indicate that the response time of services is significantly reduced in the environment of decoys with dynamic attributes. For example, Nginx's response time in a static host is twice as high as dynamic, and an Apache dynamic server can still respond to an intruder's attack even if a static server fails. Therefore, the results reported in the article give grounds to assert the possibility of using the solution in the infrastructure of information systems at the public and private levels

The object of the study is to evaluate the quality of the frequency distribution of aircraft, which characterizes the effectiveness of radar surveillance of aircraft and determines the effectiveness of their control using radio signals. The frequency resolution of an aircraft is usually studied using the frequency ambiguity function for a coherent packet of radio pulses. However, there is a problem of estimating phase fluctuations, which is caused by the heterogeneity of the propagation of radio pulses, which affects the functioning of radar stations under different atmospheric conditions. A feature of the study is the development of theoretical provisions for the process of detection and radio control of single aircraft under the organized action of swarms. A normalized frequency ambiguity function is obtained, which takes into account the transformations caused by the radial motion of the aircraft. The calculations made it possible to estimate the range of changes in the frequency distribution under the condition of the additive effect of the internal noise of the radar receiver and the multiplicative effect of the cartelized phase fluctuations of the control radio signal. The statistical characteristics of phase fluctuations of radio pulses were obtained, under which their influence on the operation of radio technical control and radar systems is the most significant. Such statistical characteristics are important for the theory of radar location and of practical importance for the improvement of radio control of objects. Method is proposed for numerical evaluation of the influence of atmospheric disturbances on the frequency distribution function of aircraft during flight. This method is a convenient tool for analyzing the quality of the frequency distribution of a radar station in various conditions of radar surveillance of single aircraft in their organized swarm action.