A New N-gram Feature Extraction-Selection Method for Malicious Code

N-grams are the basic features commonly used in sequence-based malicious code detection methods in computer virology research. The empirical results from previous works suggest that, while short length n-grams are easier to extract, the characteristics of the underlying executables are better represented in lengthier n-grams. However, by increasing the length of an n-gram, the feature space grows in an exponential manner and much space and computational resources are demanded. And therefore, feature selection has turned to be the most challenging step in establishing an accurate detection system based on byte n-grams. In this paper we propose an efficient feature extraction method where in order to gain more information; both adjacent and non-adjacent bigrams are used. Additionally, we present a novel boosting feature selection method based on genetic algorithm. Our experimental results indicate that the proposed detection system detects virus programs far more accurately than the best earlier known methods.