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:trophy:DataCon大数据安全分析大赛，2019年方向二（恶意代码检测）冠军源码、2020年方向五（恶意代码分析）季军源码

Jupyter Notebook 98.55% Python 1.43% Shell 0.02%

datacon's Introduction

DataCon:beers:

                        _   .-')
                        ( '.( OO )_
                        ,--.      .-'),-----.  .-'),-----. ,--.   ,--.)
                        |  |.-') ( OO'  .-.  '( OO'  .-.  '|   `.'   |
                        |  | OO )/   |  | |  |/   |  | |  ||         |
                        |  |`-' |\_) |  |\|  |\_) |  |\|  ||  |'.'|  |
                        (|  '---.'  \ |  | |  |  \ |  | |  ||  |   |  |
                        |      |    `'  '-'  '   `'  '-'  '|  |   |  |
                        `------'      `-----'      `-----' `--'   `--'

DataCon2019大数据安全分析大赛方向二（恶意代码检测）冠军方案:rose::rose:，详细思路分享见知乎，DataCon2020大数据安全分析大赛方向五（恶意代码分析）季军方案，详细思路分享见知乎，由于比赛时间仓促代码写得比较混乱，还请各位读者多多见谅！

DataCon2019综合积分榜排名（部分）

源码

stage1

stage2

DataCon2020综合积分榜排名（部分）

源码

[get_id.py]: 获取测试集的文件名
[get_raw_test_data.py]: 获取测试集的原始字符串
[get_raw_train_data.py]: 获取训练集的原始字符串
[test_train_model.py]: 测试训练的模型
[yield_end_result.py]: 生成最终提交的结果
[yield_features.py]: 由原始字符串生成特征矩阵
[yield_train_model.py]: 生成训练模型
[plot.py]: 绘图模块
[t_sne.py]: 降维可视化模块

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