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Detection of Ransomware using Random Forest, Support Vector Machine and Gradient Boosting Techniques
| International Journal of Applied Information Systems |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 12 - Number 42 |
| Year of Publication: 2024 |
| Authors: Adejumo Ibitola Elizabeth, Olaniyi Abiodun Ayeni |
10.5120/ijais2024451963
|
Adejumo Ibitola Elizabeth, Olaniyi Abiodun Ayeni . Detection of Ransomware using Random Forest, Support Vector Machine and Gradient Boosting Techniques. International Journal of Applied Information Systems. 12, 42 ( Mar 2024), 63-70. DOI=10.5120/ijais2024451963
Abstract
The internet’s introduction and subsequent growth have made it possible to connect people worldwide, and this trend is continuing numerous benefits result from this, including connectivity and communication as well as the broadcast and transmission of information. The cyberspace, the concept of the space within which all internet and telecommunication activities take place has become an important resource. As it is shared across the world, all information transmitted within and through this space is fair game for any who is capable of intercepting it. The aim of this research is to detect crypto-ransomware and locker. There are many means of attempting this. However, one of the simpler ideas may be to neglect the cyberspace completely. Rather than attempt to intercept signals, or spam/overload servers, it is possible to intercept the information right on the computer system it originates on.
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