关键词: 堆栈式稀疏降噪自编码网络（sSDAN）, JavaScript恶意代码, 机器学习

Abstract: For the problem that it is difficult for traditional machine learning feature extraction methods to explore the deep essential features of JavaScript malicious code, a JavaScript malicious code detection method based on stacked sparse denoising autoencoder network (sSDAN) is proposed. Firstly, JavaScript malicious code is quantized. Through adding sparsity limitation to autoencoder network, and noise with a certain probability distribution is added for learning and training of noise dyeing, the automatic encoder model can obtain the feature expressions of different levels of data. Then, by unsupervised layer by layer greedy pre-training and supervised fine-tuning process, the deeper features of effective denoising are obtained. Finally, Softmax function is used to classify the features. Experimental results show that the sparse noise reduction autoencoder classification algorithm has a good classification ability for JavaScript, and its accuracy is higher than that of traditional machine learning models, e.g. it is 0.717% higher than that of the random forest method, and 2.237% higher than that of the SVM (support vector machine) method.

Key words: stacked sparse denoising autoencoder network (sSDAN), JavaScript malicious code, machine learning