Ransomware Detection System Based on Machine Learning
In every day, there is a great growth of the Internet and smart devices connected to the network. On the other hand, there is an increasing in number of malwares that attacks networks, devices, systems and apps. One of the biggest threats and newest attacks in cybersecurity is Ransom Software (Ranso...
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| Autores principales: | , |
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| Formato: | article |
| Lenguaje: | AR EN |
| Publicado: |
College of Education for Pure Sciences
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/6ba065d614084de5b410b21616fcf49c |
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| Sumario: | In every day, there is a great growth of the Internet and smart devices connected to the network. On the other hand, there is an increasing in number of malwares that attacks networks, devices, systems and apps. One of the biggest threats and newest attacks in cybersecurity is Ransom Software (Ransomware). Although there is a lot of research on detecting malware using machine learning (ML), only a few focuses on ML-based ransomware detection. Especially attacks targeting smartphone operating systems (e.g., Android) and applications. In this research, a new system was proposed to protect smartphones from malicious apps through monitoring network traffic. Six ML methods (Random Forest (RF), k-Nearest Neighbors (k-NN), Multi-Layer Perceptron (MLP), Decision tree (DT), Logistic Regression (LR), and eXtreme Gradient Boosting (XGB)) are applied on CICAndMal2017 dataset which consists of benign and various kinds of android malware samples. A 603288 benign and ransomware samples were extracted from this collection. Ransomware samples are collected from 10 different families. Several types of feature selection techniques have been used on the dataset. Finally, seven performance metrics were used to determine the best one of feature selection and ML classifiers for ransomware detection. The experiments results imply that DT and XGB outperforms other classifiers with best detection accuracy are more than (99.30%) and (99.20%) for (DT) and (XGB) respectively. |
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