Learning and Classification of Malware Behavior
作者： Konrad Rieck , Thorsten Holz , Carsten Willems , Patrick Düssel , Pavel Laskov
DOI: 10.1007/978-3-540-70542-0_6
关键词: Computer security 、 Software 、 Computer virus 、 Malware 、 Exploit 、 The Internet 、 Trojan 、 Machine learning 、 Artificial intelligence 、 Honeypot 、 Computer science 、 Discriminative model
摘要: … for classification of malware and propose a method for learning and discrimination of malware behavior. Our method proceeds in three stages:(a) behavior of collected malware is …
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acm.org 参考文章(38)
Xuxian Jiang, Dongyan Xu, Collapsar: a VM-based architecture for network attack detention center usenix security symposium. pp. 2- 2 ,(2004)
Ulrich Bayer, Christopher Kruegel, Engin Kirda, TTAnalyze: A Tool for Analyzing Malware Proceedings of the European Institute for Computer Antivirus Research Annual Conference,2006. ,(2006)
Nello Cristianini, John Shawe-Taylor, Kernel Methods for Pattern Analysis ,(2004)
Galen Hunt, Doug Brubacher, Detours: binary interception of Win32 functions conference on usenix windows nt symposium. pp. 14- 14 ,(1999)
Thorsten Joachims, Learning to Classify Text Using Support Vector Machines ,(2002)
Mihai Christodorescu, Somesh Jha, Static analysis of executables to detect malicious patterns usenix security symposium. pp. 12- 12 ,(2003) , 10.21236/ADA449067
OL Mangasarian, A Smola, P Bartlett, B Schölkopf, D Schuurmans, Advances in Large Margin Classifiers MIT Press. ,(2000)
Engin Kirda, Richard A. Kemmerer, Christopher Kruegel, Greg Banks, Giovanni Vigna, Behavior-based spyware detection usenix security symposium. pp. 19- ,(2006)
Fabien Pouget, Leurre.com: on the advantages of deploying a large scale distributed honeypot platform ,(2005)
Peter Szor, The Art of Computer Virus Research and Defense ,(2005)