Analysis of modern scientific opinion on the study of the development and counteraction of cybercrime
DOI:
https://doi.org/10.46299/j.isjea.20240305.09Keywords:
cyber security, cyber crimes, phishing, DDoS attacks, Internet, cyber security trainingAbstract
The work provides an analysis of modern scientific thought in the field of cyber security. It has been found that there are different cybercrimes and cybercriminals often target specific targets for cyberattacks for different reasons. Cybercrimes are not limited by geographical boundaries and can occur all over the world. The prevalence of specific types of cybercrime can vary from country to country, depending on factors such as economic conditions, the level of Internet use and the general development of the phenomenon in the region. Phishing, hacking, DDoS attacks, SQL injections, zero-day exploits, cross-site scripting, attacks on Internet of Things (IoT) devices, cross-site request forgery (CSRF) are common cybercrimes in the financial sector in different countries with different methods in developed countries and developing countries. Anyone can become a victim of cybercrime: individual Internet users, businesses and corporations, educational institutions, government agencies, and others. Subsequently, there are many different methods of cyber security training that are put into practice. However, these training efforts are not effective enough, and one of the often cited reasons is user onboarding issues. Essentially, users are not engaging with the training provided to the extent necessary to get the proper benefit from the training. While the acceptance and implementation of particular training methods is discussed in the scientific literature, there is little coherent research on the factors that influence user adaptation.References
Grace Odette Boussi, Gupta, H., & Syed Akhter Hossain. (2024). A machine learning model for predicting phishing websites. International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering, 14(4), 4228–4228. https://doi.org/10.11591/ijece.v14i4.pp4228-4238
Davidian, M., Kiperberg, M., & Vanetik, N. (2024). Early Ransomware Detection with Deep Learning Models. Future Internet, 16(8), 291–291. https://doi.org/10.3390/fi16080291
Cen, M., Deng, X., Jiang, F., & Doss, R. (2024). Zero-Ran sniff: A zero-day ransomware early detection method based on zero-shot learning. Computers & Security, 142, 103849–103849. https://doi.org/10.1016/j.cose.2024.103849
Dennik Baltuttis, & Teubner, T. (2024). Effects of Visual Risk Indicators on Phishing Detection Behavior: An Eye-Tracking Experiment. Computers & Security, 103940–103940. https://doi.org/10.1016/j.cose.2024.103940
Rana Abu Bakar, Lorenzo De Marinis, Cugini, F., & Paolucci, F. (2024). FTG-Net-E: A hierarchical ensemble graph neural network for DDoS attack detection. Computer Networks, 250, 110508–110508. https://doi.org/10.1016/j.comnet.2024.110508
Lai, T., Farid, F., Bello, A., & Fariza Sabrina. (2024). Ensemble learning based anomaly detection for IoT cybersecurity via Bayesian hyperparameters sensitivity analysis. Cybersecurity, 7(1). https://doi.org/10.1186/s42400-024-00238-4
Arnoldas Budžys, Kurasova, O., & Medvedev, V. (2024). Deep learning-based authentication for insider threat detection in critical infrastructure. Artificial Intelligence Review, 57(10). https://doi.org/10.1007/s10462-024-10893-1
Farzana Quayyum, & Letizia Jaccheri. (2025). CyberFamily: A collaborative family game to increase children’s cybersecurity awareness. Entertainment Computing, 52, 100826–100826. https://doi.org/10.1016/j.entcom.2024.100826
Farzana Quayyum. (2024). Co-designing cybersecurity-related stories with children: Perceptions on cybersecurity risks and parental involvement. Entertainment Computing, 100753–100753. https://doi.org/10.1016/j.entcom.2024.100753
Morrow, E. (2024). Scamming Higher Ed: An Analysis of Phishing Content and Trends. Computers in Human Behavior, 108274–108274. https://doi.org/10.1016/j.chb.2024.108274
László Bognár, & László Bottyán. (2024). Evaluating Online Security Behavior: Development and Validation of a Personal Cybersecurity Awareness Scale for University Students. Education Sciences, 14(6), 588–588. https://doi.org/10.3390/educsci14060588
Abdeslam Rehaimi, Yassine Sadqi, Maleh, Y., Gurjot Singh Gaba, & Andrei Gurtov. (2024). Towards a federated and hybrid cloud computing environment for sustainable and effective provisioning of cyber security virtual laboratories. Expert Systems with Applications, 124267–124267. https://doi.org/10.1016/j.eswa.2024.124267
Spatafora, A., Wagemann, M., Sandoval, C., Manfred Leisenberg, & Vaz, C. (2024). An Educational Escape Room Game to Develop Cybersecurity Skills. Computers, 13(8), 205–205. https://doi.org/10.3390/computers13080205
Cigdem Avci, Bedir Tekinerdogan, & Cagatay Catal. (2024). Design tactics for tailoring transformer architectures to cybersecurity challenges. Cluster Computing. https://doi.org/10.1007/s10586-024-04355-0
Wesam Fallatah, Joakim Kävrestad, & Furnell, S. (2024). Establishing a Model for the User Acceptance of Cybersecurity Training. Future Internet, 16(8), 294–294. https://doi.org/10.3390/fi16080294
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Elina Dolia
This work is licensed under a Creative Commons Attribution 4.0 International License.