Ransomware Detection using Machine and Deep Learning Approaches

Due to the advancement and easy accessibility to computer and internet technology, network security has become vulnerable to hacker threats. Ransomware is a frequently used malware in cyber-attacks to trick the victim users to expose sensitive and private information to the attackers. Consequently, victims may not be able to access their data any longer until they pay a ransom for stolen files or data. Different methods have been introduced to overcome these issues. It is evident through an extensive literature review that some lexical features are not always sufficient to detect categories of malicious URLs. This paper proposes a model to detect Ransomware using machine and deep learning approaches. This model was introduced as a novel feature for classification using the idea that starts with “https://www.” This feature was not considered in the earlier papers on malicious URLs identification. In addition, this paper introduced a novel dataset that consists of 405,836 records. Two main experiments were carried out utilizing malicious URL features to defend Ransomware using the proposed dataset. Moreover, to enhance and optimize the experimental accuracy, various hyper-parameters were tested on the same dataset to define the optimal factors of every method. According to the comparative and experimental results of the applied classification techniques, the proposed model achieved the best performance at 99.8% accuracy rate for detecting malicious URLs using machine and deep learning.

@article{Alsaidi2022,
  title     = {Ransomware Detection using Machine and Deep Learning Approaches},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {11},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Ramadhan A. M. Alsaidi and Wael M.S. Yafooz and Hashem Alolofi and Ghilan Al-Madhagy Taufiq-Hail and Abdel-Hamid M. Emara and Ahmed Abdel-Wahab},
  doi       = {10.14569/IJACSA.2022.0131112},
  url       = {https://doi.org/10.14569/IJACSA.2022.0131112}
}