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:	Omar Ahmed, Omar Al-Dabbagh
Formato:	article
Lenguaje:	AR EN
Publicado:	College of Education for Pure Sciences 2021
Materias:	malware,, ,،,؛ransomware,, ,،,؛static and dynamic analysis,, ,،,؛network traffic,, ,،,؛ml algorithms Education L Science (General) Q1-390
Acceso en línea:	https://doaj.org/article/6ba065d614084de5b410b21616fcf49c
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spelling	oai:doaj.org-article:6ba065d614084de5b410b21616fcf49c2021-12-01T14:54:26ZRansomware Detection System Based on Machine Learning1812-125X2664-253010.33899/edusj.2021.130760.1173https://doaj.org/article/6ba065d614084de5b410b21616fcf49c2021-12-01T00:00:00Zhttps://edusj.mosuljournals.com/article_169020_42fe53102bf4e09a04af71bf83c95bfb.pdfhttps://doaj.org/toc/1812-125Xhttps://doaj.org/toc/2664-2530In 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.Omar AhmedOmar Al-DabbaghCollege of Education for Pure Sciencesarticlemalware,,,،,؛ransomware,,,،,؛static and dynamic analysis,,,،,؛network traffic,,,،,؛ml algorithmsEducationLScience (General)Q1-390ARENمجلة التربية والعلم, Vol 30, Iss 5, Pp 86-102 (2021)
institution	DOAJ
collection	DOAJ
language	AR EN
topic	malware,, ,،,؛ransomware,, ,،,؛static and dynamic analysis,, ,،,؛network traffic,, ,،,؛ml algorithms Education L Science (General) Q1-390
spellingShingle	malware,, ,،,؛ransomware,, ,،,؛static and dynamic analysis,, ,،,؛network traffic,, ,،,؛ml algorithms Education L Science (General) Q1-390 Omar Ahmed Omar Al-Dabbagh Ransomware Detection System Based on Machine Learning
description	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.
format	article
author	Omar Ahmed Omar Al-Dabbagh
author_facet	Omar Ahmed Omar Al-Dabbagh
author_sort	Omar Ahmed
title	Ransomware Detection System Based on Machine Learning
title_short	Ransomware Detection System Based on Machine Learning
title_full	Ransomware Detection System Based on Machine Learning
title_fullStr	Ransomware Detection System Based on Machine Learning
title_full_unstemmed	Ransomware Detection System Based on Machine Learning
title_sort	ransomware detection system based on machine learning
publisher	College of Education for Pure Sciences
publishDate	2021
url	https://doaj.org/article/6ba065d614084de5b410b21616fcf49c
work_keys_str_mv	AT omarahmed ransomwaredetectionsystembasedonmachinelearning AT omaraldabbagh ransomwaredetectionsystembasedonmachinelearning
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Ransomware Detection System Based on Machine Learning

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