Bitcoin Heist Ransomware Attack Prediction Using Data Science Process
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 learnin...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Subjects: | |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04056.pdf |
| _version_ | 1827897037963657216 |
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| author | Sathya T. N Keertika S Shwetha Upodhyay Deepti Muzafar Hasanov |
| author_facet | Sathya T. N Keertika S Shwetha Upodhyay Deepti Muzafar Hasanov |
| author_sort | Sathya T. |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T22:43:32Z |
| format | Article |
| id | doaj.art-b4f0f2ec6c044b95af7f13572fc8927c |
| institution | Directory Open Access Journal |
| issn | 2267-1242 |
| language | English |
| last_indexed | 2024-03-12T22:43:32Z |
| publishDate | 2023-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | E3S Web of Conferences |
| spelling | doaj.art-b4f0f2ec6c044b95af7f13572fc8927c2023-07-21T09:28:35ZengEDP SciencesE3S Web of Conferences2267-12422023-01-013990405610.1051/e3sconf/202339904056e3sconf_iconnect2023_04056Bitcoin Heist Ransomware Attack Prediction Using Data Science ProcessSathya T.0N Keertika1S Shwetha2Upodhyay Deepti3Muzafar Hasanov4Department of Computer Science and Engineering,Prince Shri Venkateshwara Padmavathy Engineering CollegeDepartment of Computer Science and Engineering,Prince Shri Venkateshwara Padmavathy Engineering CollegeDepartment of Computer Science and Engineering,Prince Shri Venkateshwara Padmavathy Engineering CollegeDepartment of Computer Science & Engineering, IES College Of TechnologyTashkent State Pedagogical UniversityIn 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.https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04056.pdfbitcoin heistransomware attackmachine learningpredictionwhitexg boostvoting classifiermontreal cryptxxxmontreal cryptolockerpaduacryptowall princeton cerberprinceton lockyrandom forest classifierlogistic regression |
| spellingShingle | Sathya T. N Keertika S Shwetha Upodhyay Deepti Muzafar Hasanov Bitcoin Heist Ransomware Attack Prediction Using Data Science Process E3S Web of Conferences 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 |
| title | Bitcoin Heist Ransomware Attack Prediction Using Data Science Process |
| title_full | Bitcoin Heist Ransomware Attack Prediction Using Data Science Process |
| title_fullStr | Bitcoin Heist Ransomware Attack Prediction Using Data Science Process |
| title_full_unstemmed | Bitcoin Heist Ransomware Attack Prediction Using Data Science Process |
| title_short | Bitcoin Heist Ransomware Attack Prediction Using Data Science Process |
| title_sort | bitcoin heist ransomware attack prediction using data science process |
| topic | 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 |
| url | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/36/e3sconf_iconnect2023_04056.pdf |
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