Ransomware Classification and Detection With Machine Learning Algorithms

TLDR

Malicious ransomware attacks threaten computer systems, data centers, and mobile applications, and traditional anti‑ransomware solutions struggle to counter newly created sophisticated threats, making advanced techniques essential. The authors propose a feature‑selection framework that employs various machine‑learning algorithms, including neural‑network architectures, to classify ransomware security levels for detection and prevention. They applied Decision Tree, Random Forest, Naïve Bayes, Logistic Regression, and Neural Network classifiers to a curated set of features and evaluated the models on a single ransomware dataset. Results show that Random Forest classifiers achieve higher accuracy, F‑beta, and precision than the other methods.

Abstract

Malicious attacks, malware, and ransomware families pose critical security issues to cybersecurity, and it may cause catastrophic damages to computer systems, data centers, web, and mobile applications across various industries and businesses. Traditional anti-ransomware systems struggle to fight against newly created sophisticated attacks. Therefore, state-of-the-art techniques like traditional and neural network-based architectures can be immensely utilized in the development of innovative ransomware solutions. In this paper, we present a feature selection-based framework with adopting different machine learning algorithms including neural network-based architectures to classify the security level for ransomware detection and prevention. We applied multiple machine learning algorithms: Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR) as well as Neural Network (NN)-based classifiers on a selected number of features for ransomware classification. We performed all the experiments on one ransomware dataset to evaluate our proposed framework. The experimental results demonstrate that RF classifiers outperform other methods in terms of accuracy, F -beta, and precision scores.