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Reseach Article

Detection of Ransomware using Random Forest, Support Vector Machine and Gradient Boosting Techniques

by Adejumo Ibitola Elizabeth, Olaniyi Abiodun Ayeni
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

@article{ 10.5120/ijais2024451963,
author = { Adejumo Ibitola Elizabeth, Olaniyi Abiodun Ayeni },
title = { Detection of Ransomware using Random Forest, Support Vector Machine and Gradient Boosting Techniques },
journal = { International Journal of Applied Information Systems },
issue_date = { Mar 2024 },
volume = { 12 },
number = { 42 },
month = { Mar },
year = { 2024 },
issn = { 2249-0868 },
pages = { 63-70 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number42/detection-of-ransomware-using-random-forest-support-vector-machine-and-gradient-boosting-techniques/ },
doi = { 10.5120/ijais2024451963 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-03-02T00:26:43.764924+05:30
%A Adejumo Ibitola Elizabeth
%A Olaniyi Abiodun Ayeni
%T Detection of Ransomware using Random Forest, Support Vector Machine and Gradient Boosting Techniques
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 42
%P 63-70
%D 2024
%I Foundation of Computer Science (FCS), NY, USA
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.

References
  1. Ade Kurniawan and Imam Riadi (2018) “Detection and Analysis Cyber Ransomware Based on Network Forensics Behavior”, International Journal of Network Security. Pp. 2-3
  2. Ayeni Olaniyi Abiodun (2022), A Supervised Machine Learning Algorithm for Detecting Malware. Journal of Internet Technology and Secured Transactions (JITST). Pp. 765
  3. Ban Mohammed Khammas (2020), Ransomware Detection using Random Forest Technique. Pp. 325-330
  4. Darshana U., Jaume M., Marzia Z., and Srinivas S. (2019). Gradient Boosting Feature Selection with Machine Learning Classifiers for Intrusion Detection on Power Grids. IEEE Transactions on Network and Service Management. Pp. 3-5.
  5. Drew Conway and John Myles White (2012) Machine Learning for Hackers. First edition http://oreilly.com/catalog/errata.csp?isbn=9781449303716 O’Reilly Media, Inc. Pp. 275-278.
  6. Fayez Tarsha Kurdi, et al (2021), Random Forest Machine Learning Technique for Automatic Vegetation Detection and Modelling in LiDAR Data. International Journal of Environmental Sciences & Natural Resources. Pp. 001.
  7. Firoz Khan, Cornelius Ncube, Lakshmana Kumar Ramasamy (2020), A Digital DNA Sequencing Engine for Ransomware Detection Using Machine Learning. Pp. 119710 – 119718.
  8. Manabu Hirano and Ryotaro Kobayashi (2019) ‘Machine Learning Based Ransomware Detection Using Storage Access Patterns Obtained from Live-forensic Hypervisor” Conference Paper · October 2019. Pp. 2-7.
  9. Sana Aurangzeb, Muhammad Aleem, Muhammad Azhar Iqbal and Muhammad Arshad Islam “Ransomware: A Survey and Trends” uploaded 2020. Pp. 2-9
  10. Shaila Sharmeen, Yahye Abukar Ahmed, Shamsul Huda, Bari. Koçer, and Mohammad Mehedi Hassan (2020), Avoiding Future Digital Extortion Through Robust Protection Against Ransomware Threats Using Deep Learning Based Adaptive Approaches. Pp. 24522 – 24524
  11. Te-Min Liu, Da-Yu Kao and Yun-Ya Chen, (2020), LooCipher Ransomware Detection Using Lightweight Packet Characteristics. Pp. 1677 – 1683
  12. Umara Urooj, Bander Ali Saleh Al-rimy, Anazida Zainal, Fuad A. Ghaleb and Murad A. Rassam. (2021), Ransomware Detection Using the Dynamic Analysis and Machine Learning: A Survey and Research Directions. Pp. 2
  13. Xueqin Zhang and Shinan Zhu. (2022), Dual Generative Adversarial Networks Based Unknown Encryption Ransomware Attack Detection. Pp. 901 and 912.
  14. Yagiz Y. (2022). Personality Types and Ransomware Victimisation. Pp 2.
Index Terms

Computer Science
Information Sciences
Machine learning Algorithm
Ransomware

Keywords

Random Forest Support vector Machine Gradient boosting Algorithm