A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Machine Learning
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Data Mining Methods for Detection of New Malicious Executables
SP '01 Proceedings of the 2001 IEEE Symposium on Security and Privacy
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing)
Comparison of feature selection and classification algorithms in identifying malicious executables
Computational Statistics & Data Analysis
Learning to Detect and Classify Malicious Executables in the Wild
The Journal of Machine Learning Research
Introduction to Information Retrieval
Introduction to Information Retrieval
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Machine learning methods were successfully applied in recent years for detecting new and unseen computer viruses. The viruses were, however, detected in small virus loader files and not in real infected executable files. We created data sets of benign files, virus loader files and real infected executable files and represented the data as collections of n-grams. Our results indicate that detecting viruses in real infected executable files with machine learning methods is nearly impossible in the n-gram representation. This statement is underpinned by exploring the n-gram representation from an information theoretic perspective and empirically by performing classification experiments with machine learning methods.