├── .gitignore
├── README.md
├── conf
└── filter.csv
├── csv
└── test.csv
├── demo.png
├── etl.py
├── init.py
├── logo.ico
├── main.py
├── requirements.txt
├── server.py
└── web
├── index.html
└── js
├── echarts.min.js
└── jquery-3.4.1.min.js
/.gitignore:
--------------------------------------------------------------------------------
1 | /.idea/
2 | /__pycache__/
3 | /build/
4 | /dist/
5 | /venv/
6 | /csv/
7 | /web/sub_title.ini
8 | /web/tmp.json
9 | *.spec
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/README.md:
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1 | # SOC_Sankey_Generator
2 |
3 | ## 简介
4 | 这是一款从SOC日志中进行数据ETL与数据可视化的工具,可以快速将日志呈现为Sankey图,适合安全日报、周报、月报中使用;也适合HW行动中防守方每日汇报。
5 |
6 | ## 环境&依赖
7 | python3
8 | pandas(必选,用于处理csv)
9 | pyinstaller(可选,exe打包用)
10 |
11 | ## 用法
12 | 将日志文件整理成表头为:**源地址,目的地址,事件名称,事件数** 的CSV格式文件,放于csv目录下
13 | 运行mian.py或者main.exe按提示输入完成数据处理,最后浏览器访问 http://127.0.0.1:8900 即可。
14 |
15 | ## 展示
16 | 
17 |
18 | ## 过滤器用法
19 | 本工具支持包含与排除两种正则过滤器,请在conf目录下的filter.csv文件中进行配置。
20 | 配置说明为:
21 |
22 | >第一列:过滤模式[in=包含,ex=排除]
23 | >第二列:字段列号[0=源地址,1=目的地址,2=攻击方式]
24 | >第三列:匹配值[正则匹配]
25 | >第四列:备注
26 |
27 |
28 | 例如:
29 | ```ex,0,114.114.114.114,备注```
30 | 为:排除源地址是114.114.1114.114的事件
31 |
32 | ```in,2,暴力破解,备注 ```
33 | 为:仅查看事件名称中包含暴力破解的事件
34 |
35 | 当过滤器文件内容发生改动时,工具会自动对数据进行重新整理,可直接刷新网页页面。
36 |
37 | ## 打包命令
38 | ```pyinstaller -i logo.ico -F main.py ```
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/conf/filter.csv:
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https://raw.githubusercontent.com/LennyLeng/SOC_Sankey_Generator/9c26829c4697f20da9c9a06b1f20da87a06d27f4/conf/filter.csv
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/csv/test.csv:
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https://raw.githubusercontent.com/LennyLeng/SOC_Sankey_Generator/9c26829c4697f20da9c9a06b1f20da87a06d27f4/csv/test.csv
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/demo.png:
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https://raw.githubusercontent.com/LennyLeng/SOC_Sankey_Generator/9c26829c4697f20da9c9a06b1f20da87a06d27f4/demo.png
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/etl.py:
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1 | #coding=utf8
2 |
3 | import pandas as pd
4 | import json
5 | import re
6 |
7 | def get_filter(csv_file):
8 | filter_list = []
9 | data = pd.read_csv(csv_file, encoding='gbk', header=0)
10 |
11 | for data_row in data.values:
12 | if (data_row[0].startswith('#')):
13 | continue
14 | dict = {}
15 | dict['filter_type'] = data_row[0]
16 | dict['col_index'] = int(data_row[1])
17 | dict['pattern_val'] = data_row[2]
18 | dict['tip'] = data_row[3]
19 | filter_list.append(dict)
20 | return filter_list
21 |
22 |
23 | def generate_json(csv_file, limit, filter_list):
24 | print('开始抽取整理数据...')
25 | # 读取csv文件,thousands用于处理千分位分割符,header用于指定第一行为表头
26 | data = pd.read_csv(csv_file, encoding='gbk', header=0, thousands=',')
27 | #按照事件数量降序排列
28 | data = data.sort_values(by='事件数',ascending=False)
29 |
30 | # 生成打环判定源地址集合,集合set具有不重复特征
31 | i0_set_tmp = set()
32 | i1_set_tmp = set()
33 |
34 | links_tmp = {}
35 | nodes_tmp = set()
36 | links = []
37 | nodes = []
38 |
39 | # 生成links,nodes_tmp
40 |
41 | i = 0
42 | for data_row in data.values:
43 | # data_row[0] => 源地址
44 | # data_row[1] => 目的地址
45 | # data_row[2].replace(" ", "") => 事件名称
46 | # data_row[3] => 事件数
47 | if (limit == '' or i < int(limit)):
48 |
49 | #删除首尾空白不可见符
50 | data_row[0] = (str)(data_row[0])
51 | data_row[0] = data_row[0].strip()
52 |
53 | data_row[1] = (str)(data_row[1])
54 | data_row[1] = data_row[1].strip()
55 |
56 | data_row[2] = (str)(data_row[2])
57 | data_row[2] = data_row[2].strip()
58 |
59 | #处理事件名称中的字符空格
60 | data_row[2] = data_row[2].replace(" ", "")
61 |
62 | #filter list policy 开始
63 | is_exclude = False
64 | for filter in filter_list:
65 | try:
66 | if(filter['filter_type'] == 'ex' and re.search(filter['pattern_val'], data_row[filter['col_index']])):
67 | print(data_row[0], '\t=>\t', data_row[2][:10] + '…', '\t=>\t', data_row[1], '\t|\t',str(data_row[3]),
68 | '\t',filter['tip'],'\t忽略')
69 | is_exclude = True
70 | break
71 |
72 | if (filter['filter_type'] == 'in' and not re.search(filter['pattern_val'],data_row[filter['col_index']])):
73 | print(data_row[0], '\t=>\t', data_row[2][:10] + '…', '\t=>\t', data_row[1], '\t|\t',
74 | str(data_row[3]),
75 | '\t', '未'+filter['tip'], '\t忽略')
76 | is_exclude = True
77 | break
78 |
79 | except:
80 | pass
81 |
82 | if(is_exclude):
83 | continue
84 | # filter list policy 结束
85 |
86 | if (data_row[2] == '' or data_row[2] == '空值'):
87 | print(data_row[0], '\t=>\t', data_row[2][:10] + '…', '\t=>\t', data_row[1], '\t|\t', str(data_row[3]),
88 | '\t事件名称为空\t修正')
89 | data_row[2] = '空名称'
90 |
91 | if (data_row[0] == data_row[1] or data_row[1] == '空值'):
92 | print(data_row[0], '\t=>\t', data_row[2][:10] + '…', '\t=>\t', data_row[1], '\t|\t', str(data_row[3]),
93 | '\t源目相同或者目的为空\t修正')
94 | data_row[1] = '0.0.0.0'
95 | i1_set_tmp.add(data_row[1])
96 |
97 | if (data_row[0] == '空值'):
98 | print(data_row[0], '\t=>\t', data_row[2][:10] + '…', '\t=>\t', data_row[1], '\t|\t', str(data_row[3]),
99 | '\t源为空\t修正')
100 | data_row[0] = '0.0.0.0'
101 | i0_set_tmp.add(data_row[0])
102 |
103 | if (data_row[1] in i0_set_tmp ):
104 | print(data_row[0], '\t=>\t', data_row[2][:10] + '…', '\t=>\t', data_row[1], '\t|\t', str(data_row[3]),
105 | '\t出现打环情况\t忽略')
106 | continue
107 |
108 | if (data_row[0] in i1_set_tmp ):
109 | print(data_row[0], '\t=>\t', data_row[2][:10] + '…', '\t=>\t', data_row[1], '\t|\t', str(data_row[3]),
110 | '\t出现多级情况\t忽略')
111 | continue
112 |
113 | i0_set_tmp.add(data_row[0])
114 | i1_set_tmp.add(data_row[1])
115 |
116 | nodes_tmp.add(data_row[0])
117 | nodes_tmp.add(data_row[1])
118 | nodes_tmp.add(data_row[2])
119 |
120 | tmp = list()
121 | tmp.append(data_row[0])
122 | tmp.append(data_row[2])
123 | tmp = json.dumps(tmp)
124 | if (tmp in links_tmp):
125 | links_tmp[tmp] += data_row[3]
126 | else:
127 | links_tmp[tmp] = data_row[3]
128 |
129 | tmp = list()
130 | tmp.append(data_row[2])
131 | tmp.append(data_row[1])
132 | tmp = json.dumps(tmp)
133 | if (tmp in links_tmp):
134 | links_tmp[tmp] += data_row[3]
135 | else:
136 | links_tmp[tmp] = data_row[3]
137 |
138 | else:
139 | #print(data_row[0], data_row[1],data_row[2],data_row[3], '!!!')
140 | break
141 | i += 1
142 |
143 |
144 | # 生成nodes
145 | for data_row in nodes_tmp:
146 | dic = {}
147 | dic['name'] = data_row
148 | nodes.append(dic)
149 |
150 | for data_row in links_tmp:
151 | tmp = json.loads(data_row)
152 | dic = {}
153 | dic['source'] = tmp[0]
154 | dic['target'] = tmp[1]
155 | dic['value'] = links_tmp[data_row]
156 | links.append(dic)
157 |
158 | data = {'nodes': nodes, 'links': links}
159 | print('抽取整理数据完成!')
160 | return json.dumps(data)
161 |
162 | if __name__ == '__main__':
163 | filter_list = get_filter('conf/filter.csv')
164 | json_data = generate_json('csv/test.csv', '', filter_list)
165 | with open('web/tmp.json', 'w') as f:
166 | f.write(json_data)
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/init.py:
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1 | #coding=utf8
2 | import etl
3 | import os
4 |
5 | def init():
6 | print("启明星辰SOC日志Sankey图分析生成工具V1.5.2")
7 | # 1.0 基本功能
8 | # 1.1 修复snakeyjson数据原到目的的数值不一致问题
9 | # 1.2 增加白名单排除配置文件
10 | # 1.3 修复filter正则匹配问题,使用更宽松的search代替match
11 | # 1.31 修复源ip未空情况
12 | # 1.4 修复i0源地址集排除逻辑,打环判定更精确;修复http.server存在缓存,刷新数据不是最新的情况;修复filter文件不在时程序报错的情况。
13 | # 1.5 重构i0源地址集排除逻辑,优化padas读取csv文件逻辑,使用thousands用于处理千分位分割符,使用header用于指定第一行为表头
14 | # 1.5.1 新增多级排除逻辑,优化padas读取config文件逻辑
15 | # 1.5.2 bugfix 极端情况字段存在float情况,强制转换至str
16 |
17 | print("作者:Lenny\n")
18 |
19 | # 选择文件
20 | print('将启明SOC日志文件整理成表头为:[源地址,目的地址,事件名称,事件数]的CSV格式文件,放于csv目录下')
21 | csv_files = dict()
22 | data_row = 1
23 | for file in os.listdir('csv'):
24 | if (file[-4:] == '.csv'):
25 | print("[%d] %s" % (data_row, file))
26 | csv_files[str(data_row)] = file
27 | data_row += 1
28 | select_index = input('输入要加载的文件序号[1-%d]:' % (data_row - 1))
29 | print("已选择:%s" % (csv_files[str(select_index)]))
30 | # 输入日期
31 | date = input('输入图表副标题:')
32 | with open('web/sub_title.ini', 'w', encoding='utf-8') as f:
33 | f.write(date)
34 |
35 | # 输入提取条数
36 | limit = input('输入提取条数(不输入默认为全部提取,建议50条):')
37 |
38 | return (csv_files[str(select_index)], limit)
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/logo.ico:
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https://raw.githubusercontent.com/LennyLeng/SOC_Sankey_Generator/9c26829c4697f20da9c9a06b1f20da87a06d27f4/logo.ico
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/main.py:
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1 | #coding=utf8
2 |
3 | import etl
4 | import server
5 | import init
6 | import os
7 | import time
8 | import multiprocessing
9 | import traceback
10 |
11 | def generate_json_and_write_file(csv_file, limit):
12 | filter_chang_time = float(0)
13 | while True:
14 | try:
15 | if(filter_chang_time < os.stat('conf/filter.csv').st_mtime):
16 | filter_list = etl.get_filter('conf/filter.csv')
17 | json_data = etl.generate_json('csv/' + csv_file, limit, filter_list)
18 | with open('web/tmp.json', 'w') as f:
19 | f.write(json_data)
20 | print("web服务已启动,打开浏览器访问http://127.0.0.1:%d" % (server.PORT))
21 | filter_chang_time = os.stat('conf/filter.csv').st_mtime
22 | except:
23 | #当filter文件不存在时,重置文件改变时间
24 | traceback.print_exc()
25 | filter_chang_time = float(0)
26 | time.sleep(1)
27 |
28 | def run():
29 | (csv_file, limit) = init.init()
30 | json_p = multiprocessing.Process(target=generate_json_and_write_file, args=(csv_file, limit))
31 | json_p.start()
32 | http_p = multiprocessing.Process(target=server.run)
33 | http_p.start()
34 |
35 | def debug():
36 | (csv_file, limit) = init.init()
37 | http_p = multiprocessing.Process(target=server.run)
38 | http_p.start()
39 | generate_json_and_write_file(csv_file, limit)
40 |
41 | if __name__ == '__main__':
42 | multiprocessing.freeze_support()
43 | run()
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/requirements.txt:
--------------------------------------------------------------------------------
1 | pandas
2 | pyinstaller
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/server.py:
--------------------------------------------------------------------------------
1 | import http.server
2 | PORT = 8900
3 | DIRECTORY = "web"
4 | def run():
5 | class Handler(http.server.SimpleHTTPRequestHandler):
6 | def __init__(self, *args, **kwargs):
7 | super().__init__(*args, directory=DIRECTORY, **kwargs)
8 |
9 | def end_headers(self):
10 | self.send_cacheless_headers()
11 | http.server.SimpleHTTPRequestHandler.end_headers(self)
12 |
13 | def send_cacheless_headers(self):
14 | self.send_header("Cache-Control", "no-cache, no-store, must-revalidate")
15 | self.send_header("Pragma", "no-cache")
16 | self.send_header("Expires", "0")
17 |
18 | try:
19 | httpd = http.server.HTTPServer(("", PORT), Handler)
20 | httpd.serve_forever()
21 | except KeyboardInterrupt:
22 | print('接收到关闭指令,退出程序')
23 | httpd.shutdown()
24 |
25 | if __name__ == '__main__':
26 | run()
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/web/index.html:
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1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
50 |
51 |
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/web/js/jquery-3.4.1.min.js:
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