84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
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# -*- coding : utf-8-*-
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import pandas as pd
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import os
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import re
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DOS2019_FLOWS = {'attackers': ['172.16.0.5'],
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'victims': ['192.168.50.1', '192.168.50.4']}
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input_dir = "cicddos2019/input"
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label_dir = "cicddos2019/label"
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feature_latitude = 40 + 1 # 第一个元素是label
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def get_label(srcIP, dstIP):
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if (srcIP == "172.16.0.5" and dstIP == "192.168.50.1") or (srcIP == "192.168.50.1" and dstIP == "172.16.0.5"):
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return 1
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elif (srcIP == "172.16.0.5" and dstIP == "192.168.50.4") or (srcIP == "192.168.50.4" and dstIP == "172.16.0.5"):
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return 1
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else:
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return 0
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def pre_process(dir, label_dir):
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files = os.listdir(dir)
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files.sort(key=lambda x: int(x[0:1]))
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for filename in files:
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feature_ls = []
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flow = {}
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input_path = dir+"/"+filename
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df = pd.read_csv(input_path, encoding='ISO-8859-1')
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print("------------")
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print("processing file: {} ".format(input_path))
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for index, row in df.iterrows():
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if index % 100000 ==0:
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print("processing index:{}".format(index))
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try:
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srcIP = str(row["Source"])
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dstIP = str(row["Destination"])
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except:
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print(srcIP)
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print(dstIP)
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continue
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if not re.match(r"^(?:[0-9]{1,3}\.){3}[0-9]{1,3}$", srcIP) or not re.match(r"^(?:[0-9]{1,3}\.){3}[0-9]{1,3}$", srcIP):
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continue
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protocol = row["Protocol"]
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# get label
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label = get_label(srcIP, dstIP)
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# key
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key = srcIP+dstIP
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reverse_key = dstIP+srcIP
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if not flow.__contains__(key) and not flow.__contains__(reverse_key):
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# 创建一个flow
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flow[key] = [label, "-"+protocol]
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elif flow.__contains__(key):
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# key添加到flow里面
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if len(flow[key]) >= feature_latitude:
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feature_ls.append(flow.pop(key))
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flow[key] = [label, "-"+protocol]
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flow[key].append("-"+protocol)
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flow[key][0] = flow[key][0] or label
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else:
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# reverse key
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if len(flow[reverse_key]) >= feature_latitude:
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feature_ls.append(flow.pop(reverse_key))
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flow[reverse_key] = [label, "+"+protocol]
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flow[reverse_key].append("+"+protocol)
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# 更新type数量
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flow[reverse_key][0] = flow[reverse_key][0] or label
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# write to csv file
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total_data = pd.DataFrame(data=feature_ls)
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# print(total_data)
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label_path=label_dir+"/"+filename
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total_data.to_csv(label_path, index=False,
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encoding="utf-8", sep=',', mode='w', header=True)
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return
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pre_process(input_dir, label_dir)
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