Main Article Content

Abstract

With the advent and subsequent explosion of the internet, global connectivity has been achieved, and is on the rise. This provides a host of advantages such as connectivity and communication, information broadcast and transmission, amongst others. This however introduces a new set of challenges: the safety and protection of these communication channels amongst them. Information has always been power, and the widespread mature of information only results in the widespread attempts to procure it, sometimes via illegal channels.  In view of this, this research aims at detecting Crypto-ransomware and locker ransomware. Data was collected from an open repository and cleaned. The cleaned data was then split into tests, train sets and validation which was used to train a number of ML models based on the: Random Forest algorithm, Support Vector Machine (SVM) and Gradient boosting algorithm. Ransomware is one of the well-known ways and frequent use which cyber-attackers use in infecting their victims, either through phishing or drive download. Attackers will create an email pretending to be from a genuine resource and send it to their targeted victims. However, this research illustrated how to combat crypto-ransomware and locker ransomware. Implementing the machine learning algorithm, the system can detect ransomware under 30’s, giving computer users over 90% assurance of their system for ransomware free.

Keywords

Gradient Boosting Algorithm Machine Learning Random Forest Ransomware Support Vector Machine

Article Details

How to Cite
[1]
O. Abiodun Ayeni and I. Adejumo, “SVM Ransomware Detection Using Machine Learning Algorithm”, bit-cs, vol. 5, no. 2, pp. 74-84, Jun. 2024.

References

  1. [1]. Abdullahi Arabo, Remi Dijoux,Timothee poulain,Gregoire Chevailer, (2020), Detecting Ransomware Using Process Behavior Analysis. Pp. 289 and 295
  2. [2]. 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.
  3. [3]. 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.
  4. [4]. Eduardo Berrueta, Daniel Morato, Eduardo Magana, Mikel Izal (2022), Crypto-ransomware detection using machine learning models in file-sharing network scenarios with encrypted traffic. Pp. 1-3
  5. [5]. Fayez Tarsha Kurdi (2021), Random Forest Machine Learning Technique for Automatic Vegetation Detection and Modelling in LiDAR Data. International Journal of Environmental Sciences & Natural Resources. Pp. 001. (Fayez Tarsha
  6. [6]. Juan A. Herrera-Silva and Myriam Hernández-Álvarez (2023), Dynamic Feature Dataset for Ransomware Detection Using Machine Learning Algorithms. Pp. 1-21
  7. [7]. SH Kok, Azween Abdullah, NZ Jhanjhi and Mahadevan Supramaniam (2019) Ransomware, Threat and Detection Techniques: A Review. ILCSNS International Journal of computer Science and Network Security, Vol. 19.2, Pp. 138-139.
  8. [8]. Kok S.H. and Mahadevan (2019) Prevention of Crypto-Ransomware Using a Pre-Encryption Detection Algorithm. Articles www.mdpi.com/journal/computers. Pp. 2-5
  9. [9]. 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.
  10. [10]. Olaniyi Abiodun Ayeni, Otasowie Owolafe, Olabiyi Akinsola (2021), Malware Detection using Machine Learning, Conference paper. Pp. 86
  11. [11]. Samah Alsoghyer and Iman Almomani (2019) Ransomware Detection System for Android Applications. Pp. 1-31.
  12. [12]. SH Kok, Azween Abdullah, NZ Jhanjhi and Mahadevan Supramaniam (2019) Ransomware, Threat and Detection Techniques: A Review. ILCSNS International Journal of computer Science and Network Security, Vol. 19.2, Pp. 138-139.
  13. [13]. SH Kok, Azween Abdullah, NZ Jhanjhi and Mahadeyan supramaniam. (2019), Ransomware, threat and Detection Techniques: A review. Pp. 1-11
  14. [14]. Subash Poudyal, Dipankar Dasgupta, Zahid Akhtar, Kishor Datta Gupta., (2019), A Multi-Level Ransomware Detection Framework using Natural Language Processing and Machine Learning. Pp. 2-9
  15. [15]. Xiang G., et al. (2019). An Improved Random Forest Algorithm for Predicting Employee Turnover. Research Article. Pp. 2-5