About Us
Our Blog
Contact UsMethods for the prevention of computer crimes in organizations: A review
DOI:
https://doi.org/10.47909/dtr.03Keywords:
computer crimes, systematic review, computer securityAbstract
The present review, then, has the purpose of analyzing the importance of computer crime prevention methods in society, through a systematic bibliographic review, in which valuable information and relevant results are collected, at the same time recommending ways to prevent cybercrime attacks to be applied at the organizational level and at the individual level. Search criteria, article selection, and article evaluation define the literature review process. This systematic literature review searched for articles using three digital databases, used search strings to collect multiple articles, and selected relevant articles based on year, article type, and title, also focusing on articles related to social engineering and phishing. The databases used for this research are: Scopus, IEEE and ScienceDirect. Based on this systematic review of the literature, one investigation was found on the prevention protocol to configure the exchange of information in a social network, three investigations on user studies, three investigations on concepts of prevention of social engineering attacks, three research on engineering attack prevention model, one research on social engineering attack prevention method, four research on other methods.
Downloads
References
Abe, N., Institute of Electrical and Electronics Engineers, & IEEE Computer Society. (2018). MPMPA: A Mitigation and Prevention Model for Social Engineering Based Phishing attacks on Facebook. IEEE International Conference on Big Data, 5040–5048.
Abe, N., & Soltys, M. (2019). Deploying health campaign strategies to defend against social engineering threats. Procedia Computer Science, 159, 824–831. https://doi.org/10.1016/j.procs.2019.09.241 DOI: https://doi.org/10.1016/j.procs.2019.09.241
Albladi, S. M., & Weir, G. R. S. (2018). User characteristics that influence judgment of social engineering attacks in social networks. Human-Centric Computing and Information Sciences, 8(1). https://doi.org/10.1186/s13673-018-0128-7 DOI: https://doi.org/10.1186/s13673-018-0128-7
Albladi, S. M., & Weir, G. R. S. (2020a). Predicting individuals’ vulnerability to social engineering in social networks. Cybersecurity, 3(1). https://doi.org/10.1186/s42400-020-00047-5 DOI: https://doi.org/10.1186/s42400-020-00047-5
Albladi, S. M., & Weir, G. R. S. (2020b). Predicting individuals’ vulnerability to social engineering in social networks. Cybersecurity, 3(1). https://doi.org/10.1186/s42400-020-00047-5 DOI: https://doi.org/10.1186/s42400-020-00047-5
Algarni, A., Xu, Y., & Chan, T. (2017). An empirical study on the susceptibility to social engineering in social networking sites: The case of Facebook. European Journal of Information Systems, 26(6), 661–687. https://doi.org/10.1057/s41303-017-0057-y DOI: https://doi.org/10.1057/s41303-017-0057-y
Andryukhin, A. A. (2019). Phishing Attacks and Preventions in Blockchain Based Projects. Proceedings - 2019 International Conference on Engineering Technologies and Computer Science: Innovation and Application, EnT 2019, 15–19. https://doi.org/10.1109/EnT.2019.00008 DOI: https://doi.org/10.1109/EnT.2019.00008
Athulya A A, & Praveen K. (2020). Towards the Detection of Phishing Attacks. Proceedings of the Fourth International Conference on Trends in Electronics and Informatics.
Bhargava, S., & Yablonovitch, E. (2015). Lowering HAMR near-field transducer temperature via inverse electromagnetic design. IEEE Transactions on Magnetics, 51(4). https://doi.org/10.1109/TMAG.2014.2355215 DOI: https://doi.org/10.1109/TMAG.2014.2355215
Brands, J., & van Doorn, J. (2022). The measurement, intensity and determinants of fear of cybercrime: A systematic review. Computers in Human Behavior, 127. https://doi.org/10.1016/j.chb.2021.107082 DOI: https://doi.org/10.1016/j.chb.2021.107082
Fatima, R., Yasin, A., Liu, L., & Wang, J. (2019). How persuasive is a phishing email? A phishing game for phishing awareness. Journal of Computer Security, 27(6), 581–612. https://doi.org/10.3233/JCS-181253 DOI: https://doi.org/10.3233/JCS-181253
Hatfield, J. M. (2018). Social engineering in cybersecurity: The evolution of a concept. Computers and Security, 73, 102–113. https://doi.org/10.1016/j.cose.2017.10.008 DOI: https://doi.org/10.1016/j.cose.2017.10.008
Java S, Linsha B F, Riaz S, Jeet K M, & Mushtaq A. (2019). Detection of Online Manipulation to Prevent Users Victimization. Amity International Conference on Artificial Intelligence (AICAI). DOI: https://doi.org/10.1109/AICAI.2019.8701330
Junger, M., Montoya, L., & Overink, F. J. (2017). Priming and warnings are not effective to prevent social engineering attacks. Computers in Human Behavior, 66, 75–87. https://doi.org/10.1016/j.chb.2016.09.012 DOI: https://doi.org/10.1016/j.chb.2016.09.012
Kumar N, Dabas P, & Komal. (2019). Detection and Prevention of Profile Cloning in Online Social Networks. IEEE International Conference on Signal Processing. DOI: https://doi.org/10.1109/ISPCC48220.2019.8988394
Lallie, H. S., Shepherd, L. A., Nurse, J. R. C., Erola, A., Epiphaniou, G., Maple, C., & Bellekens, X. (2021). Cyber security in the age of COVID-19: A timeline and analysis of cyber-crime and cyber-attacks during the pandemic. Computers and Security, 105. https://doi.org/10.1016/j.cose.2021.102248 DOI: https://doi.org/10.1016/j.cose.2021.102248
Milani, R., Caneppele, S., & Burkhardt, C. (2022). Exposure to Cyber Victimization: Results from a Swiss Survey. Deviant Behavior, 43(2), 228–240. https://doi.org/10.1080/01639625.2020.1806453 DOI: https://doi.org/10.1080/01639625.2020.1806453
Naidoo, R. (2020). A multi-level influence model of COVID-19 themed cybercrime. European Journal of Information Systems, 29(3), 306–321. https://doi.org/10.1080/0960085X.2020.1771222 DOI: https://doi.org/10.1080/0960085X.2020.1771222
Rameem Zahra, S., Ahsan Chishti, M., Iqbal Baba, A., & Wu, F. (2021). Detecting Covid-19 chaos driven phishing/malicious URL attacks by a fuzzy logic and data mining based intelligence system. Egyptian Informatics Journal. https://doi.org/10.1016/j.eij.2021.12.003 DOI: https://doi.org/10.1016/j.eij.2021.12.003
Rastogi, M., Chhetri, A., Singh, D. K., & Rajan V, G. (2021). Survey on Detection and Prevention of Phishing Websites using Machine Learning. 2021 International Conference on Advance Computing and Innovative Technologies in Engineering, ICACITE 2021, 78–82. https://doi.org/10.1109/ICACITE51222.2021.9404714 DOI: https://doi.org/10.1109/ICACITE51222.2021.9404714
Sánchez, D., Domingo-Ferrer, J., & Martínez, S. (2018). Co-utile disclosure of private data in social networks. Information Sciences, 441, 50–65. https://doi.org/10.1016/j.ins.2018.02.010 DOI: https://doi.org/10.1016/j.ins.2018.02.010
Singh C, & Smt.Meenu. (2020). Phishing Website Detection Based on Machine Learning: A Survey. International Conference on Advanced Computing and Communication Systems (ICACCS). DOI: https://doi.org/10.1109/ICACCS48705.2020.9074400
Suryan, V. S. K., & Sreeya, B. (2019). Public opinion on cyber crime. International Journal of Innovative Technology and Exploring Engineering, 8(11), 3198–3200. https://doi.org/10.35940/ijitee.K2517.0981119 DOI: https://doi.org/10.35940/ijitee.K2517.0981119
Syafitri, W., Shukur, Z., Mokhtar, U. A., Sulaiman, R., & Ibrahim, M. A. (2022). Social Engineering Attacks Prevention: A Systematic Literature Review. IEEE Access, 10, 39325–39343. https://doi.org/10.1109/access.2022.3162594 DOI: https://doi.org/10.1109/ACCESS.2022.3162594
Syed, R. (2019). Enterprise reputation threats on social media: A case of data breach framing. Journal of Strategic Information Systems, 28(3), 257–274. https://doi.org/10.1016/j.jsis.2018.12.001 DOI: https://doi.org/10.1016/j.jsis.2018.12.001
Yasin, A., Fatima, R., Liu, L., Yasin, A., & Wang, J. (2019). Contemplating social engineering studies and attack scenarios: A review study. Security and Privacy, 2(4). https://doi.org/10.1002/spy2.73 DOI: https://doi.org/10.1002/spy2.73
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Junior Villa-Soto (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
This is an open access article distributed under the terms of the CC BY-NC 4.0, which permits copying and redistributing the material in any medium or format, adapting, transforming, and building upon the material as long as the license terms are followed.