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» Decision tree and instance-based learning for label ranking
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JMLR
2006
132views more  JMLR 2006»
14 years 11 months ago
Learning to Detect and Classify Malicious Executables in the Wild
We describe the use of machine learning and data mining to detect and classify malicious executables as they appear in the wild. We gathered 1,971 benign and 1,651 malicious execu...
Jeremy Z. Kolter, Marcus A. Maloof
ACSAC
2003
IEEE
15 years 5 months ago
Behavioral Authentication of Server Flows
Understanding the nature of the information flowing into and out of a system or network is fundamental to determining if there is adherence to a usage policy. Traditional methods...
James P. Early, Carla E. Brodley, Catherine Rosenb...
ISCI
2007
130views more  ISCI 2007»
14 years 11 months ago
Learning to classify e-mail
In this paper we study supervised and semi-supervised classification of e-mails. We consider two tasks: filing e-mails into folders and spam e-mail filtering. Firstly, in a sup...
Irena Koprinska, Josiah Poon, James Clark, Jason C...
DIMVA
2008
15 years 1 months ago
Learning and Classification of Malware Behavior
Malicious software in form of Internet worms, computer viruses, and Trojan horses poses a major threat to the security of networked systems. The diversity and amount of its variant...
Konrad Rieck, Thorsten Holz, Carsten Willems, Patr...
ICDM
2009
IEEE
112views Data Mining» more  ICDM 2009»
15 years 6 months ago
Spatio-temporal Multi-dimensional Relational Framework Trees
—The real world is composed of sets of objects that move and morph in both space and time. Useful concepts can be defined in terms of the complex interactions between the multi-...
Matthew Bodenhamer, Samuel Bleckley, Daniel Fennel...