Ensuring Financial System Sustainability: Combating Hybrid Threats through Anti-Money Laundering and Counter-Terrorist Financing Measures

Korauš, Antonín; Jančíková, Eva; Gombár, Miroslav; Kurilovská, Lucia; Černák, Filip

doi:10.3390/jrfm17020055

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…The dynamic approach thus ensures the long-term sustainability of the proposed approach in detecting security incidents in the system, which in general may consist of several permitted steps but whose sequence as a process in the system is suspicious. The monitored system, in our case, is any system creating a log of its operation, so the proposed approach applies to a wide range of systems, particularly in cyberspace, and the proposed approaches can thus significantly help in the fight against hybrid threats (Korauš et al, 2024).…”

Section: Hybrid Threatsmentioning

confidence: 99%

Reduction of cybersecurity risk via evaluating users' behaviour

Korauš,

Špitalský,

Török

et al. 2024

JESI

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Since the 1990s, process analysis has attained a fundamental position among business management approaches. With the gradual development and expansion of digitalization in businesses that have begun to use advanced information systems, a demand also arose to survey the processes within companies, including retrospectively from the digital records of information systems. This requirement laid the foundation for the emergence of the scientific discipline known today as Process Mining. In the presented article, we introduce its basic concepts and point out the possibility of using them in the field of security analysis of the log of a general system, which creates digital records of its operation (a so-called journal or log). The result of using Process Mining methods is identifying unrecorded processes running in a system and various deviations from the expected system operation, which may signal security threats to the system itself or its operator. In the battle against hybrid threats, many resources are explicitly devoted to protecting cyberspace. The approach proposed in this article allows a system to be analysed as a whole, identifying patterns of behaviour that would not otherwise arouse suspicion in individual steps but, as a sequence of separate steps (processes), do not fall into the expected pattern of system behaviour. This can be used as a long-term sustainable concept in the fight against hybrid threats. An analysis of a system's behavior can be built on continuous "learning" by labelling newly discovered processes as safe or unsafe, ensuring the long-term sustainability of this approach. The main advantage of the proposed analyses is that they run as an oversight of the system itself, analysing it only based on records from its event log. Therefore, no interventions are needed in the architecture and source code of the analysed system, and the analyses do not affect its operation or data.

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Section: Hybrid Threatsmentioning

confidence: 99%

Reduction of cybersecurity risk via evaluating users' behaviour

Korauš,

Špitalský,

Török

et al. 2024

JESI

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“…Bitcoin, the first and most well-known cryptocurrency, was introduced in 2009, and since then, thousands of other cryptocurrencies have been created, each with its unique features and purposes (Lipman, 2023). On the other hand, money laundering is the process of disguising the origins of illegally obtained money, typically by passing it through a complex sequence of transfers or commercial transactions (Levi and Reuter, 2011;Korauš et al, 2024). This illegal activity allows criminals to make illicit funds appear legitimate.…”

Section: Introductionmentioning

confidence: 99%

Integrating Machine Learning in Anti-Money Laundering through Crypto: A Comprehensive Performance Review

Japinye

2024

EJAAFR

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The integration of machine learning (ML) algorithms in Anti-Money Laundering (AML) practices has garnered significant attention due to its potential to enhance the detection and prevention of illicit activities in the cryptocurrency ecosystem. This systematic literature review analysed the effectiveness of integrating ML algorithms in detecting and preventing crypto laundering activities, identify the most frequently used ML algorithms, examine trends in publication and research methodologies, and discuss key challenges and constraints associated with integrating ML technologies into AML frameworks. A comprehensive search strategy was employed to identify relevant studies, resulting in the inclusion of 52 articles published between 2019 and 2023. The findings reveal a growing interest in the field, with a notable increase in publications in recent years. Traditional ML models such as Logistic Regression, Random Forest, and Support Vector Machine (SVM) remain prevalent, while deep learning models like Multilayer Perceptrons (MLP) and Long Short-Term Memory (LSTM) networks are gaining popularity. Graph Convolutional Networks (GCNs) have emerged as a significant area of exploration, particularly in the context of graph data analysis in cryptocurrencies. Despite advancements in ML, cryptocurrencies continue to pose a high risk of money laundering due to the practical challenge of implementation ownership of the various ML models. Future research should focus on how these challenges will be addressed to ensure the effective and sustainable use of ML technologies in real-world AML practices.

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“…Such research conducted about hybrid threats is one way to help effectively build the resilience of security environments against hybrid threats. The relevant connecting of theoretical knowledge, applied experience, and quantitative and qualitative indicators brings the necessary value in effectively building the country's resilience to hybrid threats (Korauš et al, 2024).…”

Section: Introductionmentioning

confidence: 99%

Perception of hybrid threats by students of selected universities and building effective resistance against their effects

Korauš,

Kurilovská,

Krásná

et al. 2024

JESI

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The issue of hybrid threats is an ongoing topic that resonates with the professional and the lay public. More and more in the scope of professional discussions, we come across the definition and specification of circumstances in which the significant signs of hybrid threats are discussed and the threat they pose to society are pointed out. The study reflects the need to know and specify the understanding of "hybrid threats". It presents the results of a survey that was conducted to determine how students of two universities in the Slovak Republic (the Police Force Academy in Bratislava and the Faculty of Management, Economics and Business of the University of Prešov in Prešov) perceive and whether they even know the term "hybrid threats" itself. A significant moment in the orientation of young people to security aspects is knowledge of the definition of this term among young students who are preparing for a profession and who will form the future of the international community. Therefore, only a precise specification of the concept and a definition of the distinct elements for building effective resistance to hybrid threats at the national and international level, specifically in individual countries, will help us maintain a sense of security among individual members of society. The content of the study is constructed to fulfil the primary goal, which is to determine and present the perception of the concept of "hybrid threats" among university students, as well as to present elementary concepts associated with the issue of hybrid threats and a selected element supporting resistance to hybrid threats at the national levelstrategic communication. The proposed findings are based on quantitative and qualitative research conducted within the national project "Increasing Slovakia's resilience to hybrid threats by strengthening public administration capacities" and reflect on the current needs of the security environment at both the national and international levels. The research was conducted between May and September 2023 to obtain quantitative and qualitative indicators.

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Ensuring Financial System Sustainability: Combating Hybrid Threats through Anti-Money Laundering and Counter-Terrorist Financing Measures

Cited by 5 publications

References 43 publications

Reduction of cybersecurity risk via evaluating users' behaviour

Reduction of cybersecurity risk via evaluating users' behaviour

Integrating Machine Learning in Anti-Money Laundering through Crypto: A Comprehensive Performance Review

Perception of hybrid threats by students of selected universities and building effective resistance against their effects

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