Pre-Encryption Ransomware Detection (PERD) Taxonomy, and Research Directions: Systematic Literature Review

Today’s era is witnessing an alarming surge in ransomware attacks, propelled by the increasingly sophisticated obfuscation tools deployed by cybercriminals to evade conventional antivirus defenses. Therefore, there is a need to better detect and obfuscate viruses. This analysis embarks on a comprehensive exploration of the intricate landscape of ransomware threats, which will become even more problematic in the upcoming era. Attackers may practice new encryption approaches or obfuscation methods to create ransomware that is more difficult to detect and analyze. The damage caused by ransomware ranges from financial losses, at best paid for ransom, to the loss of human life. We presented a Systematic Literature Review and quality analysis of published research papers on the topic. We investigated 30 articles published between the year 2018 to the year 2023(H1). The outline of what has been published thus far is reflected in the 30 papers that were chosen and explained in this article. One of our main conclusions was that machine learning ML-based detection models performed better than others. Additionally, we discovered that only a small number of papers were able to receive excellent ratings based on the standards for quality assessment. To identify past research practices and provide insight into potential future guidelines in the pre-encryption ransomware detection (PERD) space, we summarized and synthesized the existing machine learning studies for this SLR. Future researchers will use this study as a roadmap and assistance to investigate the preexisting literature efficiently and effectively.

@article{Shaikh2024,
  title     = {Pre-Encryption Ransomware Detection (PERD) Taxonomy, and Research Directions: Systematic Literature Review},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Mujeeb Ur Rehman Shaikh and Mohd Fadzil Hassan and Rehan Akbar and Rafi Ullah and K.S. Savita and Ubaid Rehman and Jameel Shehu Yalli},
  doi       = {10.14569/IJACSA.2024.0150917},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150917}
}