E3S Web Conf.
Volume 399, 2023International Conference on Newer Engineering Concepts and Technology (ICONNECT-2023)
|Number of page(s)||11|
|Published online||12 July 2023|
Bitcoin Heist Ransomware Attack Prediction Using Data Science Process
1 Department of Computer Science and Engineering,Prince Shri Venkateshwara Padmavathy Engineering College, Chennai, Tamil Nadu, xxx
2 Department of Computer Science & Engineering, IES College Of Technology, Bhopal, MP 462044 India
3 Tashkent State Pedagogical University, Tashkent, Uzbekistan
In recent years, ransomware attacks have become a more significant source of computer penetration. Only general-purpose computing systems with sufficient resources have been harmed by ransomware so far. Numerous ransomware prediction strategies have been published, but more practical machine learning ransomware prediction techniques still need to be developed. In order to anticipate ransomware assaults, this study provides a method for obtaining data from artificial intelligence and machine learning systems. A more accurate model for outcome prediction is produced by using the data science methodology. Understanding the data and identifying the variables are essential elements of a successful model. A variety of machine learning algorithms are applied to the pre-processed data, and the accuracy of each technique is compared to determine which approach performed better. Additional performance indicators including recall, accuracy, and f1-score are also taken into account while evaluating the model. It uses machine learning to predict how the ransomware attack would pan out.
Key words: Bitcoin Heist / Ransomware Attack / Machine Learning / Prediction / White / XG Boost / Voting Classifier / Montreal CryptXXX / Montreal CryptoLocker / PaduaCryptoWall / Princeton Cerber / Princeton Locky / Random Forest Classifier / Logistic Regression
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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