关键词: 降维, TF-IDF-like模型, 特征差异度, JavaScript, 主成分分析（PCA）

Abstract: This paper proposes an efficient dimension reduction method to avoid the curse of dimensionality caused by using the N-gram model to identify malicious JavaScript. The method uses the TF-IDF-like model to calculate respectively the weight of features in normal samples and malicious samples, and carries out dimension reduction based on the difference feature weight. Based on many recognition algorithms, this paper compares the proposed method with the dimension reduction method based on Principal Component Analysis（PCA）. The experimental results demonstrate two conclusions. Firstly, the recognition effect of the proposed method is better than that of PCA at the same feature dimension. Secondly, when the reserved dimension exceeds a certain threshold, with the increase of the reserved dimension, the growth rate of time cost is much lower than PCA.

Key words: dimension reduction, TF-IDF-like model, different weight of features, JavaScript, Principal Component Analysis（PCA）