An Improved Protection Approach for Protecting from Ransomware Attacks

Year 2023, Issue: 1, 69 - 82, 15.08.2023

Ferhat Guvçi , Ahmet Şenol

https://doi.org/10.26650/JODA.1312412

Abstract

Ransomware is a type of malicious software that has become a significant threat to the security and availability of computer systems and data. Ransomware has found a special place in the world of malware and is the subject of many scientific studies, as it is a malicious software designed to benefit the user directly by using sensitive data of individuals or institutions. This research provides an in-depth study of ransomware, including its history and evolution. The primary objective of this research is to analyze the impact of ransomware attacks on organizations and individuals and to evaluate the effectiveness of existing countermeasures and mitigation strategies. To achieve this objective, a comprehensive review of the literature and security provider sources on ransomware was conducted and data analyzed from real-world ransomware incidents. The findings indicated that ransomware attacks are becoming more sophisticated and complex, targeting a wide range of industries and geographical regions, which poses a significant financial and reputational risk to victims. Moreover, this research showed that traditional security measures such as antivirus software, firewalls, and backups may not be sufficient to prevent or recover from ransomware attacks. Instead, artificial intelligence applications and a multi-layered defense approach that combined technical, administrative, and legal measures is necessary to reduce the likelihood and impact of ransomware incidents. Overall, this article provides a valuable contribution to the understanding of ransomware threats and the development of effective countermeasures, and contributes to the literature especially on defense methods by explaining how to apply defense methods against ransomware attacks in light of field experience.

Keywords

encryption, machine learning, malware, ransomware

References

• Askarifar, S., Rahman, N. A. A., & Osman, H. (2018). A review of latest wannacry ransomware: Actions and preventions. J. Eng. Sci. Technol, 13, 24-33. google scholar
• DFIR Report, Reports. (2020, August 31). NetWalker Ransomware in 1 Hour. https://thedfirreport.com/2020/08/31/ netwalker-ransomware-in-1-hour/ google scholar
• Dwyer, J. (2021, November 30). Understanding the Adversary: How Ransomware Attacks Happen. https:// securityintelligence.com/posts/how-ransomware-attacks-happen/ google scholar
• Fernando, D. W., Komninos, N., & Chen, T. (2020). A study on the evolution of ransomware detection using machine learning and deep learning techniques. IoT, 1(2), 551-604. google scholar
• Furnell, S., & Emm, D. (2017). The ABC of ransomware protection. Computer Fraud & Security, 2017(10), 5-11. google scholar
• Gomez-Hernandez, J. A., Sanchez-Fernandez, R., & Gartia-Teodoro, P. (2022). Inhibiting crypto-ransomware on windows platforms through a honeyfile-based approach with R-Locker. IETInformation Security, 16(1), 64-74. google scholar
• Gvozdenko, A. (2023, April). How AI will Revolutionize Ransomware Prevention. https://www.cynergy.app/cyber-research/how-ai-will-revolutionize-ransomware-prevention/4765/#:~:text=AI%2C%20on%20the%20other%20 hand,overall%20effectiveness%20of%20ransomware%20prevention google scholar
• Health Sector Cybersecurity Coordination Center (2020, September) U.S. Department of Health and Human Services, https://www.hhs.gov/sites/default/files/netwalker.pdf google scholar
• Kapoor, A., Gupta, A., Gupta, R., Tanwar, S., Sharma, G., & Davidson, I. E. (2021). Ransomware detection, avoidance, and mitigation scheme: a review and future directions. Sustainability, 14(1), 8. google scholar
• Maurya A.K, Kumar N., Agrawal A., Khan R.A(2017). Ransomware: Evolution, Target and Safety Measures. International Journal of Computer Sciences and Engineering, Volume-6,Issue-1. google scholar
• Mohurle, S., & Patil, M. (2017). A brief study of wannacry threat: Ransomware attack 2017. International Journal of Advanced Research in Computer Science, 8(5), 1938-1940. google scholar
• Richardson, R., & North, M. M. (2017). Ransomware: Evolution, mitigation and prevention. International Management Review, 13(1), 10. google scholar
• Zavarsky, P., & Lindskog, D. (2016). Experimental analysis of ransomware on windows and android platforms: Evolution and characterization. Procedia Computer Science, 94, 465-472. google scholar