In-Depth Analysis of Ransom Note Files

During recent years, many papers have been published on ransomware, but to the best of our knowledge, no previous academic studies have been conducted on ransom note files. In this paper, we present the results of a depth study on filenames and the content of ransom files. We propose a prototype to...

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Autores principales:	Yassine Lemmou, Jean-Louis Lanet, El Mamoun Souidi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	ransomware ransom note file detection identification Latent Semantic Analysis Machine Learning Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/e756da32a7414a7c82e55cda912082ec
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id	oai:doaj.org-article:e756da32a7414a7c82e55cda912082ec
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spelling	oai:doaj.org-article:e756da32a7414a7c82e55cda912082ec2021-11-25T17:17:26ZIn-Depth Analysis of Ransom Note Files10.3390/computers101101452073-431Xhttps://doaj.org/article/e756da32a7414a7c82e55cda912082ec2021-11-01T00:00:00Zhttps://www.mdpi.com/2073-431X/10/11/145https://doaj.org/toc/2073-431XDuring recent years, many papers have been published on ransomware, but to the best of our knowledge, no previous academic studies have been conducted on ransom note files. In this paper, we present the results of a depth study on filenames and the content of ransom files. We propose a prototype to identify the ransom files. Then we explore how the filenames and the content of these files can minimize the risk of ransomware encryption of some specified ransomware or increase the effectiveness of some ransomware detection tools. To achieve these objectives, two approaches are discussed in this paper. The first uses Latent Semantic Analysis (LSA) to check similarities between the contents of files. The second uses some Machine Learning models to classify the filenames into two classes—ransom filenames and benign filenames.Yassine LemmouJean-Louis LanetEl Mamoun SouidiMDPI AGarticleransomwareransom note filedetectionidentificationLatent Semantic AnalysisMachine LearningElectronic computers. Computer scienceQA75.5-76.95ENComputers, Vol 10, Iss 145, p 145 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	ransomware ransom note file detection identification Latent Semantic Analysis Machine Learning Electronic computers. Computer science QA75.5-76.95
spellingShingle	ransomware ransom note file detection identification Latent Semantic Analysis Machine Learning Electronic computers. Computer science QA75.5-76.95 Yassine Lemmou Jean-Louis Lanet El Mamoun Souidi In-Depth Analysis of Ransom Note Files
description	During recent years, many papers have been published on ransomware, but to the best of our knowledge, no previous academic studies have been conducted on ransom note files. In this paper, we present the results of a depth study on filenames and the content of ransom files. We propose a prototype to identify the ransom files. Then we explore how the filenames and the content of these files can minimize the risk of ransomware encryption of some specified ransomware or increase the effectiveness of some ransomware detection tools. To achieve these objectives, two approaches are discussed in this paper. The first uses Latent Semantic Analysis (LSA) to check similarities between the contents of files. The second uses some Machine Learning models to classify the filenames into two classes—ransom filenames and benign filenames.
format	article
author	Yassine Lemmou Jean-Louis Lanet El Mamoun Souidi
author_facet	Yassine Lemmou Jean-Louis Lanet El Mamoun Souidi
author_sort	Yassine Lemmou
title	In-Depth Analysis of Ransom Note Files
title_short	In-Depth Analysis of Ransom Note Files
title_full	In-Depth Analysis of Ransom Note Files
title_fullStr	In-Depth Analysis of Ransom Note Files
title_full_unstemmed	In-Depth Analysis of Ransom Note Files
title_sort	in-depth analysis of ransom note files
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e756da32a7414a7c82e55cda912082ec
work_keys_str_mv	AT yassinelemmou indepthanalysisofransomnotefiles AT jeanlouislanet indepthanalysisofransomnotefiles AT elmamounsouidi indepthanalysisofransomnotefiles
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In-Depth Analysis of Ransom Note Files

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