A Machine Learning Framework Developed by Leveraging the Topological Pattern Analysis of Bitcoin Addresses in Blockchain Network for Ransomware Identification
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-50815-8_2
Reference18 articles.
1. Akcora, C.G., Li, Y., Gel, Y.R., Kantarcioglu, M.: BitcoinHeist: topological data analysis for ransomware detection on the bitcoin blockchain. arXiv preprint arXiv:1906.07852 (2019)
2. Andronio, N., Zanero, S., Maggi, F.: HELDROID: dissecting and detecting mobile ransomware. In: Bos, H., Monrose, F., Blanc, G. (eds.) RAID 2015. LNCS, vol. 9404, pp. 382–404. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-26362-5_18
3. Androulaki, E., Karame, G.O., Roeschlin, M., Scherer, T., Capkun, S.: Evaluating user privacy in bitcoin. In: Sadeghi, A.R. (ed.) FC 2013. LNCS, vol. 7859, pp. 34–51. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-39884-1_4
4. Conti, M., Gangwal, A., Ruj, S.: On the economic significance of ransomware campaigns: a Bitcoin transactions perspective. Comput. Secur. 79, 162–189 (2018)
5. Di Battista, G., Di Donato, V., Patrignani, M., Pizzonia, M., Roselli, V., Tamassia, R.: Bitconeview: visualization of flows in the bitcoin transaction graph. In: 2015 IEEE Symposium on Visualization for Cyber Security (VizSec), pp. 1–8. IEEE, October 2015
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