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» Detecting worm variants using machine learning
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CORR
2011
Springer
184views Education» more  CORR 2011»
14 years 4 months ago
Metamorphic Virus Variants Classification Using Opcode Frequency Histogram
Abstract- In order to prevent detection and evade signature-based scanning methods, which are normally exploited by antivirus softwares, metamorphic viruses use several various obf...
Babak Bashari Rad, Maslin Masrom
IWAN
2004
Springer
15 years 2 months ago
Distributed Instrusion Prevention in Active and Extensible Networks
The proliferation of computer viruses and Internet worms has had a major impact on the Internet Community. Cleanup and control of malicious software (malware) has become a key prob...
Todd S. Sproull, John W. Lockwood
CVPR
2009
IEEE
1390views Computer Vision» more  CVPR 2009»
16 years 4 months ago
Stacks of Convolutional Restricted Boltzmann Machines for Shift-Invariant Feature Learning
In this paper we present a method for learning classspecific features for recognition. Recently a greedy layerwise procedure was proposed to initialize weights of deep belief ne...
Mohammad Norouzi (Simon Fraser University), Mani R...
ML
2010
ACM
155views Machine Learning» more  ML 2010»
14 years 8 months ago
On the infeasibility of modeling polymorphic shellcode - Re-thinking the role of learning in intrusion detection systems
Current trends demonstrate an increasing use of polymorphism by attackers to disguise their exploits. The ability for malicious code to be easily, and automatically, transformed in...
Yingbo Song, Michael E. Locasto, Angelos Stavrou, ...
121
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NIPS
2001
14 years 11 months ago
Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade
This paper develops a new approach for extremely fast detection in domains where the distribution of positive and negative examples is highly skewed (e.g. face detection or databa...
Paul A. Viola, Michael J. Jones