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• W2501996246 abstract "Malware poses a huge threat to society, which is heavily dependent on computer technology. Traces of malicious activity can be identified through digital forensics techniques. Digital forensics is performed in a semi-automatic manner. Forensic personnel have to administrate the forensic tools and the process of searching for digital evidence on suspect, confiscated computers. This becomes a daunting task when multiple machines are to be analyzed and the data volumes increase. Analysis of common characteristics in a set of multiple computers can be used to improve knowledge and to detect anomalies and thereby malware. This Master thesis proposes a correlation method for the automatic identification of malware traces across multiple computers. Through the use of existing digital forensics methods and data mining techniques, correlations between multiple machines are used to improve the efficiency and effectiveness of detecting traces of malware." @default.
• W2501996246 created "2016-08-23" @default.
• W2501996246 creator A5061489131 @default.
• W2501996246 date "2010-01-01" @default.
• W2501996246 modified "2023-09-23" @default.
• W2501996246 title "Cross-Computer Malware Detection in Digital Forensics" @default.
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• W2501996246 hasPublicationYear "2010" @default.
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