# Ransomware Detection Model Based on Adaptive Graph Neural Network Learning +
## 3 Methodology
Our research holds significant practical value for cybersecurity professionals. The ADC-TextGCN model's efficient capability in detecting ransomware offers a robust tool against network threats, enabling more precise and effective identification and prevention of potential ransomware attacks. Given the increasingly complex digital environment, frequent ransomware attacks, and continuously evolving methods, the significance of this scientific advancement is particularly notable. It has established a solid defensive line for protecting information security. In our research, we emphasize two future research directions: improving model scalability and exploring the use of attention mechanisms, highlighting the tremendous potential for further progress in this area. As network security threats continue to evolve, so must our strategies and methods to counter these threats. Our research provides profound insights and powerful tools for cybersecurity professionals and researchers, laying a solid foundation for addressing future security challenges. Investigation, software, writing of original draft, validation, methodology, computing resources and automated data collection, G.Y. ; conceptualization, methodology, supervision, and writing--review and editing. J.L. ; data curation, Y.S. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Translational Application Project of the \"Wise Eyes Action\" (Project No. F2B6A194). We express our deepest gratitude to these organizations for their generous support. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The datasets presented in this study are available at www.github.com/Yangggy/ABC (accessed on 18 March 2024). **Acknowledgments:** We would like to express our gratitude to [PERSON] (The Sixth Research Institute of China Electronics and Information Industry Corporation) for providing computational resources and support. All authors have read and agreed to the published version of the manuscript. **Conflicts of Interest:** The authors declare no conflicts of interest. ## References

* _[PERSON] et al. (2019)_ [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] On the social science of ransomware: Technology, security, and society. _Comp. Strategy_**2019**, _38_, 347-370. [CrossRef]
* [PERSON] (2017) [PERSON]; [PERSON] Ransomware: Evolution, mitigation and prevention. _Int. Manag. Rev._**2017**, _13_, 10. * [PERSON] et al. (2019) [PERSON]; [PERSON]; [PERSON] WannaCry ransomware: Analysis of infection, persistence, recovery prevention and propagation mechanisms. _J. Telecommun. Inf. Technol._**2019**, \(1\), 113-124. [CrossRef]
* [PERSON] (2019) [PERSON] WannaCry Aftershock. Sophos. 2019. Available online: [[https://www.sophos.com/en-us/medialibrary/PDFs/technical-papers/WannaCry-Aftershock.pdf](https://www.sophos.com/en-us/medialibrary/PDFs/technical-papers/WannaCry-Aftershock.pdf)]([https://www.sophos.com/en-us/medialibrary/PDFs/technical-papers/WannaCry-Aftershock.pdf](https://www.sophos.com/en-us/medialibrary/PDFs/technical-papers/WannaCry-Aftershock.pdf)) (accessed on 6 March 2024). * [PERSON] (2017) [PERSON] Privacy, confidentiality, and security of health care information: Lessons from the recent WannaCry cyberattack. _World Neurosurg._**2017**, _104_, 972-974. [CrossRef]
* [PERSON] et al. (2020) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] The QoS and privacy trade-off of adversarial deep learning: An evolutionary game approach. _Comput. Secur._**2020**, _96_, 101876. [CrossRef]
* [PERSON] (2009) [PERSON]; [PERSON] The effectiveness of anti-malware tools. _Comput. Fraud Secur._**2009**, _2009_, 12-13. [CrossRef]
* [PERSON] et al. (2018) [PERSON]; [PERSON]; [PERSON] R-Locker: Thwarting ransomware action through a honeyfile-based approach. _Comput. Secur._**2018**, _73_, 389-398. [CrossRef]
* [PERSON] et al.