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2022amwd-webshelldetection's Introduction

2022AMWD-WebshellDetection

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消融实验

ablation_study/路径下是报告中的消融实验的源码。 数据是官方给定的train.zip和train.csv, 如在本地运行,需要将zip文件解压后放入对应目录下的tctrain/train/,csv文件放入tctrain即可(和比赛提供的路径一致)。

Graph Convolution Network

ablation_study/gcn: 使用GCN模块进行分类。

  • gcn_split1.json和gcn_split2.json分别是按照4:1:1和4:1:50划分的数据集进行实验的参数。
  • 进入模块, 运行python main.py --config "gcn_split1.json" 即可,也可以自定义参数设置的json文件。
  • statistic.py用于统计最后的实验结果。

Graph Attention Network

ablation_study/gat: 使用GAT模块进行分类。

  • configs/gat/split1configs/gat/split2分别是按照4:1:1和4:1:50划分的数据集进行实验的参数配置文件。
  • 进入ablation_study/gat,运行nohup ./run.sh > run.log 2>&1 &
  • 运行结果将分别附加输出到configs/gat/split1/gat_split1_rslt.txt文件和configs/gat/split2/gat_split1_rslt.txt文件中

2022amwd-webshelldetection's People

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codingclaire avatar jie-re avatar

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