The current state of research in the area of identification and analysis of threats resulting from cybercrime
pdf

Keywords

cybercrime
cybersecurity
security
crime

Categories

How to Cite

Małodobry, Z., Misztal, L. and Piotrowski, S. (2026) “The current state of research in the area of identification and analysis of threats resulting from cybercrime ”, Scientific Journal of Bielsko-Biala School of Finance and Law. Bielsko-Biała, PL, 30(2). doi: 10.19192/wsfip.sj2.2022.11.

Abstract

This article examines the current state of research on cybercrime, focusing on its key actors, targets, and methods of counteraction. It highlights the growing scale, complexity, and economic significance of cyber threats, as well as the increasing role of artificial intelligence in both offensive and defensive activities. The study also identifies major research gaps and emphasizes the need for integrated and interdisciplinary approaches to effectively address cybercrime

https://doi.org/10.19192/wsfip.sj2.2022.11
pdf

References

Wall D. S., (2007) Cybercrime: The transformation of crime in the information age, Cambridge.

NASK – Państwowy Instytut Badawczy, (2023) Raport o stanie cyberbezpieczeństwa w Polsce, Warszawa.

CERT Coordination Center, (2022) Annual Threat Report, Pittsburgh.

Holt T. J., (2016) Cybercrime through an interdisciplinary lens, New York.

Leukfeldt R., Holt T. J., (2020) The human factor of cybercrime, New York.

Hadnagy C., (2018) Social engineering: The science of human hacking, Indianapolis.

Singer P. W., Friedman A., (2014) Cybersecurity and cyberwar: What everyone needs to know, Oxford.

Furnell S., (2017) Cybersecurity in the digital age, London.

Liu R., Shi J., Chen X., Lu C., (2024) Network anomaly detection and security defense technology based on machine learning: A review, London.

Sayem I. M., et al., (2024) ENIDS: A deep learning-based ensemble framework for network intrusion detection [w:] „IEEE Transactions on Network and Service Management”, no. 21(5), New York.

Sommer R., Paxson V., (2010) Outside the closed world: On using machine learning for network intrusion detection [w:] „IEEE Symposium on Security and Privacy”, Berkeley.

Behl A., et al., (2023) Cybersecurity analytics: A stochastic model for insider threat detection, Cham.

Buczak A. L., Guven E., (2016) A survey of data mining and machine learning methods for cyber security intrusion detection [w:] „IEEE Access”, New York.

Vassilev A., et al., (2025) Adversarial machine learning: A taxonomy and terminology of attacks and mitigations, Gaithersburg.

ENISA, (2024) ENISA Threat Landscape 2024, Athens.

Council of Europe, (2001/2024) Convention on Cybercrime, Strasbourg.

Dyrektywa Parlamentu Europejskiego i Rady (UE) 2022/2555 (NIS 2), (2022) Bruksela.

Rozporządzenie Parlamentu Europejskiego i Rady (UE) 2024/2847 (Cyber Resilience Act), (2024) Bruksela.

Council of the European Union, (2024) Cybersecurity package, Brussels.

Europol, (2023) Internet Organised Crime Threat Assessment (IOCTA), Haga.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Copyright (c) 2026 Zbigniew Małodobry, Laura Misztal, Sebastian Piotrowski

Downloads

Download data is not yet available.