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To perform (1) structural similarities among 2,436 cryptographic Windows ransomware samples per calendar year between 2017 and 2021 and (2) structural dissimilarities against 3,014 benign applications using machine learning classifiers.

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AhsanAyub/deep_static_ransomware_analysis

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Static-RWArmor: A Static Analysis Approach for Prevention of Cryptographic Windows Ransomware

In this project, we investigate how similar ransomware samples collected in the same calendar year are based on the Cosine Index from 2017 to 2021. Additionally, we build machine learning classifiers to effectively identify the structural dissimilarities between 2,436 ransomware samples and 3,014 benign applications.

The research project has been published at the 22nd IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom 2023).

Citing this work

If you use our implementation for academic research, you are highly encouraged to cite our paper.

@inproceedings{ayub2023static-rwarmor,
  title={Static-RWArmor: A Static Analysis Approach for Prevention of Cryptographic Windows Ransomware},
  author={Ayub, Md Ahsan and Siraj, Ambareen and Filar, Bobby and Gupta, Maanak},
  booktitle={2023 IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)},
  pages={1681--1688},
  year={2023},
  organization={IEEE}
}

The work reported in this paper has been supported by Cybersecurity Education, Research & Outreach Center (CEROC) at Tennessee Tech University. Additionally, it is partially supported by the NSF grants 2025682 and 2230609.

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To perform (1) structural similarities among 2,436 cryptographic Windows ransomware samples per calendar year between 2017 and 2021 and (2) structural dissimilarities against 3,014 benign applications using machine learning classifiers.

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