C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A selective sampling approach to active feature selection
Artificial Intelligence
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
| Hi-index | 0.00 |
Detecting unknown worms is a challenging task. Extant solutions, such as anti-virus tools, rely mainly on prior explicit knowledge of specific worm signatures. As a result, after the appearance of a new worm on the Web there is a significant delay until an update carrying the worm's signature is distributed to anti-virus tools. We propose an innovative technique for detecting the presence of an unknown worm, based on the computer operating system measurements. We monitored 323 computer features and reduced them to 20 features through feature selection. Support vector machines were applied using 3 kernel functions. In addition we used active learning as a selective sampling method to increase the performance of the classifier, exceeding above 90% mean accuracy, and for specific unknown worms 94% accuracy.