Перегляд за Автор "Kozlenko, Oleh"

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    An example of fuzzy ontology usage for risk assessment and attack impact
    (Igor Sikorsky Kyiv Polytechnic Institute, 2024) Kozlenko, Oleh
    The article discusses the use of fuzzy ontology for assessing risks and impacts of attacks in the field of information security. Fuzzy ontology, which is a formalized way of representing knowledge, offers effective solutions for processing complex and informal processes. The article substantiates the significance of fuzzy logic in structural analysis and presents an example of how new types of attacks influence the ontology. Key findings include the identification of risks associated with attacks through the application of fuzzy sets and entropy theory. The discussion highlights how these methods can enhance threat response and risk management in information systems.
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    Analysis of the core research for vendor email compromise filtering model using machine learning
    (Igor Sikorsky Kyiv Polytechnic Institute, 2023) Zibarov, Dmytro; Kozlenko, Oleh
    Vendor email compromise became one of most sophisticated types of social engineering attacks. Strengths of this malicious activity rely on basis of impersonating vendor that company working with. Thus, it is easy for attacker to exploit this trust for doing different type of data exfiltration or ransom. To mitigate risks, that come with these challenges, information security specialist should consider using different types of approaches, including machine learning, to identify anomalies in email, so further damages can be prevented. The purpose of this work lies in the identification of optimal approach for VEC-style attacks detection and optimizing these approaches with least amount of falsepositive (FP) parameters. The object of this research is different methods of text processing algorithms, including machine learning methods for detecting VEC emails. The subject of research in this paper mainly considers impact of mentioned text processing algorithms and its relation with efficiency of VEC email classification, identifying most effective approach and, also, how to improve results of such detections. Results of this paper consists of details for VEC-email attacks detection, challenges that comes with different approaches and proposed solution, that lies in using text processing techniques and agentrelated approach with main sphere of implication – machine-learning systems, that are used for identifying social-engineering attacks through email.
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    Basic concepts, approaches and fundamentals of cyber threat intelligence
    (Igor Sikorsky Kyiv Polytechnic Institute, 2022) Makovska, Maryna; Kozlenko, Oleh
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    Comparison analysis between strict ontologies and fuzzy ontologies
    (Igor Sikorsky Kyiv Polytechnic Institute, 2024) Kozlenko, Oleh
    Ontological modeling has been important in the field of cybersecurity, but with the growing use of artificial intelligence in various processes related to cybersecurity, it has become an increasingly relevant area for research every new year. Ontologies can serve as a primary source of knowledge for artificial intelligence models and as a "sequence of actions" in different processes. Typically, strictontologies were used due to their formalized structure, but they did not fully capture processes that involve fuzzy contexts of actions or results. The aim of this article is to present and analyze different ontologies, both strict and fuzzy, that are used or could be used in the field of cybersecurity and related processes, demonstrating their similarities, differences, and areas of application.
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    Fuzzy ontology structure for information leaks and ISC
    (Igor Sikorsky Kyiv Polytechnic Institute, 2019) Kozlenko, Oleh
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    Stochastic Violator Model
    (Igor Sikorsky Kyiv Polytechnic Institute, 2021) Kaczynski, Anatoliy; Kireienko, Oleksandr; Kozlenko, Oleh
    This paper introduces a new type of violator model that is based on Markov chains. It can be used as a scenario model AS IS or as a mathematical model with quantitative estimates if additional information is presented. Our aim with this paper was to develop a model that will allow to restore missing data, using existing knowledge about violator. The results show that presented scenario for general cases cover the majority of attacks and can be applied to real-life scenarios too. Summing up the results, it can be concluded that additional improvement of the model should be focused on data gathering to ensure that existing data will be enough to recover the rest.