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Mechanisms for analysis and detection of ransomware in desktop operating systems

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Detalhes bibliográficos
Resumo:Ransomware attacks have become a danger to computer systems, leading to data loss, monetary losses, and business interruptions. We propose a machine learning-based method for ransomware detection on Linux to identify these attacks. To detect ransomware activity on the system, our approach combines the file system with a predictive model. To obtain sufficient infection information we use the data from the alteration calls to the files on the file system. This data is then fed into a machine-learning algorithm. Using a dataset we collected from uninfected files and files infected with various types of ransomware and were able to achieve a high detection rate with a low false positive rate. Our methodology can be incorporated into current security programs to improve detection and defense against ransomware attacks in the Linux environment.
Autores principais:Santos, Vinicius Belloli dos
Assunto:Ransomware Malware Cybersecurity Threat detection Linux
Ano:2023
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB

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