计算机科学 ›› 2019, Vol. 46 ›› Issue (3): 197-201.doi: 10.11896/j.issn.1002-137X.2019.03.029
曹卫东,许志香,王静
CAO Wei-dong, XU Zhi-xiang, WANG Jing
摘要： 针对基于监督学习的入侵检测算法所需训练样本标签难以收集、无监督学习算法准确度不高,以及网络入侵检测中的高维数据处理的问题,提出一种基于深度生成模型的半监督入侵检测方法。该方法旨在构建合理有效的目标函数,提高模型的分类准确率及泛化能力。首先,用变分自编码(Variational Auto-Encoder,VAE)将高维原始数据双向映射至低维空间,以获得原始数据的低维表示;然后,用数据的生成模型提高单独使用有标签数据时的分类准确率。实验表明,该方法利用少量有标记数据能够取得较高的检测准确率。
中图分类号:
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