Unraveling Ransomware: Detecting Threats with Advanced Machine Learning Algorithms

In our contemporary world, the pervasive influence of information technology, computer engineering, and the Internet has undeniably catalyzed innovation, fostering unparalleled economic growth and revolutionizing education. This technological juggernaut, however, has unwittingly ushered in a parallel era of new criminal frontiers, a magnet for hackers and cybercriminals. These malevolent actors exploit the vast expanse of electronic devices and interconnected networks to perpetrate an array of cybercrimes, and among these insidious digital threats, ransomware reigns supreme. Ransomware, characterized by its ominous ability to encrypt victims' data and extort payment for its release, stands as a dire menace to individuals and organizations alike. Operating with stealth and propagating with alarming alacrity through digital networks, ransomware has emerged as a formidable adversary in the digital age. This research paper focuses on the evolving stages of ransomware, driven by cutting-edge technologies, and proposes essential methods and ideas to detect and combat this menace. The proposed methodology, anchored in Cuckoo Sandbox, PE file feature extraction, and YARA rules, orchestrates three crucial phases: data collection, feature selection, and data preprocessing, all harmonizing to strengthen our defense against this concealed cyber menace. This paper contributes to the development of effective solutions for detecting and mitigating this hidden and insidious cyber threat. This work involves the application of multiple machine learning algorithms, including LSTM, which achieves an impressive accuracy of 99% in identifying ransomware attacks.

@article{Hammadeh2023,
  title     = {Unraveling Ransomware: Detecting Threats with Advanced Machine Learning Algorithms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Karam Hammadeh and M. Kavitha},
  doi       = {10.14569/IJACSA.2023.0140952},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140952}
}