期刊论文

中文

计算机工程与科学

1007-130X

2022

44

6

994-1002

10.3969/j.issn.1007-130X.2022.06.006

国家自然科学基金(31870532)。

本校为第一机构

以AWD攻防中Webshell检测为背景,在超空间利用模糊C均值聚类分析发现了攻击向量全局稀疏、局部紧密的特点,提出了2种深度学习模型。由于GitHub收集的攻击行为多为随机获取,没有很好的针对性,所以对训练数据的长度进行了限制,并保留了有限的相关样本数量。由于一次攻击与相邻的2~4次操作紧密相关,而且攻击向量垂直方向关联特征明显,水平方向相对稳定,考虑到特征向量在传递过程中规模会减小,增加了卷积层的补零选项。针对深度学习训练曲线中的锯齿振荡现象,证明了Adam优化算法的快速计算公式,并修正了学习参数,不断消除了训练的Loss曲线中的锯齿,使得训练曲线按照指数规律平滑下降,迅速得到需要的...

Based on Webshell detection in AWD offensive and defensive competition,fuzzy C-means clustering is used to analyze Webshell in hyperspace,and find that the attack vector is globally sparse and locally closely related.Two deep learning models are proposed for Webshell detection.Since most of the Webshells collected by GitHub are obtained randomly and are not well targeted,the length of the training data is limited and a limited number of relevant samples are retained.Because one attack is closely related to the adjacent 2 to 4 operations,the attack vector has obvious correlation characteristics...