python - 使用python检测日志文件中是否存在csv列中的字符串？-6ren

python - 使用python检测日志文件中是否存在csv列中的字符串？

转载作者：太空宇宙更新时间：2023-11-04 04:30:59

如果它与 csv 文件的第一列匹配，我如何在日志文件中获取特定列？如果没有匹配则打印“Undetected”然后如果匹配:获取日志文件中的特定列。我已经解决这个问题 2 天了，请帮忙

这是我的日志文件结构:完整内容:trendx.log

1537761898  0   1   1   1537733097  1537733098  1537733097  8224    74  215552  06a60c6018a42b1db22e3bf8620861711401c4bb.crdownload TROJ.Win32.TRX.XXPE50FFF026 c:\users\administrator\desktop\downloader\download\     TRENDX  172.20.4.179    Administrator           c1f387a6f45414366755b0a1874b36ff9596d8ad        AABACACCBIiAgXWACAAAAAAAAAAAAACAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=        TSPY_HPDYRE.SM;TSPY_GOLROTED.ACS;TROJ_GEN.R021C0FFO15;TROJ_GEN.R021C0DLO15;Ransom_HPCRYPTESLA.SM2;

并采样我的 csv 文件:完整内容:sha1_vsdt.csv

SHA-1,VSDT,
0191a23ee122bdb0c69008971e365ec530bf03f5,MIME 6010-0,
02b809d4edee752d9286677ea30e8a76114aa324,Microsoft RTF 6008-0
0349e0101d8458b6d05860fbee2b4a6d7fa2038d,Adobe Portable Document Format(PDF)

我用过这个，但我不知道为什么它输出未检测到，然后从第 30 行到第 552 行开始它没有显示任何内容:

import numpy as np
import pandas as pd
import csv

#Log data into dataframe using genfromtxt
logdata = np.genfromtxt("trendx.log",invalid_raise = False,dtype=str, comments=None,usecols=np.arange(0,24))
logframe = pd.DataFrame(logdata)
#Dataframe trimmed to use only SHA1, PRG and IP
df2=(logframe[[10,14,15]]).rename(columns={10:'SHA1', 14: 'PRG',15:'IP'})


#sha1_vsdt data into dataframe using read_csv
df1=pd.read_csv("sha1_vsdt.csv",delimiter=",",error_bad_lines=False,engine = 'python',quoting=3)
#Using merge to compare the two CSV
df = pd.merge(df1, df2, left_on='SHA-1', right_on='SHA1', how='left').replace(np.nan, 'undetected', regex=True)
print(df[['SHA-1','VSDT','PRG','IP']])

输出:

0             0191a23ee122bdb0c69008971e365ec530bf03f5     ...      undetected
1             02b809d4edee752d9286677ea30e8a76114aa324     ...      undetected
2             0349e0101d8458b6d05860fbee2b4a6d7fa2038d     ...      undetected
3             035a7afca8b72cf1c05f6062814836ee31091559     ...      undetected
4             042065bec5a655f3daec1442addf5acb8f1aa824     ...      undetected
5             04939e040d9e85f84d2e2eb28343d94a50ed46ac     ...      undetected
6             04a1876724b53a016cd9e9c93735985938c91fa4     ...      undetected
7             06109df23f7d5deadf0b2c158af1f71c2997d245     ...      undetected
8             06194c240c12c51b55d2961ae287fd9628e05751     ...      undetected
9             0665de1ad83715cc6e68d00ed700c469944a5925     ...      undetected
10            067b448f4c9782489e5ff60c31c62b7059e500b2     ...      undetected
11            0688e6966b0e4a1f58d2f3de48f960fce5b42292     ...      undetected
12            0689f6f99d10dd8bf396f2d2c73ce9dcb6dcad23     ...      undetected
13            06a60c6018a42b1db22e3bf8620861711401c4bb     ...      undetected
14            0723a895a5f8b2d5d25b4303e9f04d16551791b6     ...      undetected
15            07344621cf4480c430f8931af2b2b056775af7e3     ...      undetected
16            07831df482f1a34310fc4f5a092c333eeaff4380     ...      undetected
17            08386105057cd5867480095696a5ca6701fdb8ad     ...      undetected
18            0ad5f62b4ec10397b7d13433a8dc794dc6d4f273     ...      undetected
19            0bed7d032d5c51f606befd2f10b94e5c75a6a1e3     ...      undetected
20            0c3f8d2cce9e7a6e5604b8d0c9fbe1ff6fd5cebb     ...      undetected
21            0c793b4f4e0be7f24f93786d7d4a719a7a002a0d     ...      undetected
22            0c7c2b2d05a5c712f4b9302b82fb54007210937f     ...      undetected
23            0d03da55b246252fb5b440a23943426bda965bcd     ...      undetected
24            0d592f948a4f7bfa95c7cb09faf067ce9fbc9375     ...      undetected
25            0df65d8a57c8349e044f98deda17d70d0c4f926a     ...      undetected
26            0e13d281af08954102e7caf95864ef553c7277bd     ...      undetected
27            0ede12d9c17564e803f51de4d279e84623c5a8a6     ...      undetected
28            0fc4f3a30684bb17cbcbf4e3def2ac3528a2f04c     ...      undetected
29            0fcb475fcadd8d8e3b8dd5f4376feda48c73fd24     ...      undetected
..                                                 ...     ...             ...
553           ef90b17c18c3c5960726964cff12b6d6ef22f3f4     ...      undetected
554           effbed4e7e619009def1c4322f68092eb9cc197f     ...      undetected
555           f081c8a737f87167fef83d03405c1fbe55a46986     ...      undetected
556           f1304ad198045ebb93e70252f0dda9d68acd83f1     ...      undetected
557           f14762b5ce92f2713c584140d694ce25f7beb9c2     ...      undetected
558           f187959d6afa483d18c69b9e334575781009cd31     ...      undetected
559           f1ae32a92f89f54e542973a98eb3dcbe05fe9c58     ...      undetected
560           f28217b5928e4d2fbbc5ca45bd815b1c3963bed2     ...      undetected
561           f36687584c4bc38f2aed5511930b50eea378c1bf     ...      undetected
562           f4846b38f52805ffa2d0ae392df05bbeb8fee2b5     ...      undetected
563           f4b8b762feb426de46a0d19b86f31173e0e77c2e     ...      undetected
564           f4d0cc44a8018c807b9b1865ce2dd70f027d2ceb     ...      undetected
565           f4fcbbdf8c797c96dd1a3e76baf666c319f52aa8     ...      undetected
566           f6c9b393b5148e45138f724cebf5b1e2fd8d9bc7     ...      undetected
567           f8910d7869be647d2ec6c49ddf6fef49ed0f09d0     ...      undetected
568           f90c38a3d623ea47b129b386d841614d9a290f0a     ...      undetected
569           f99c069d5ababc7001aa46a494a0400a913a109c     ...      undetected
570           f9d2c6e2438fc4571f7ea4f639b2950ddd1307e5     ...      undetected
571           fa2229ef95b9e45e881ac27004c2a90f6c6e0947     ...      undetected
572           fac66887402b4ac4a39696f3f8830a6ec34585be     ...      undetected
573           fb2086d390c1755b53580013c727398d9fb5c01b     ...      undetected
574           fb59aa51fec66f8caf409b1ca2b80e7fdaf33c61     ...      undetected
575  fc39dfde0 -X=0/0 -X=0/0 -...     ...      undetected
576           fcb12edabdb2e59916f2f84f204c3e8ec13d1135     ...      undetected
577           fcbbfeb67cd2902de545fb159b0eed7343aeb502     ...      undetected
578           fced05723f49b6d0836e065a436e8c3b8df2bc12     ...      undetected
579           fd1cada68f4a9452275d292fe4b9f76a4bd8bd8b     ...      undetected
580           fe5babc1e4f11e205457f2ec616f117fd4f4e326     ...      undetected
581           fe8c341de79168a1254154f4e4403857c6e79c46     ...      undetected
582           fe91021461e48fe82449d2ad73bcc66f6c508152     ...      undetected

这是我的预期输出:

18            0ad5f62b4ec10397b7d13433a8dc794dc6d4f273      ...           undetected
19            0bed7d032d5c51f606befd2f10b94e5c75a6a1e3      ...        Administrator
20            0c3f8d2cce9e7a6e5604b8d0c9fbe1ff6fd5cebb      ...           undetected

最佳答案

问题是 genfromtxt 省略了长度不等于 24 的行。

可能的解决方案是使用 np.arange(16):

logdata = np.genfromtxt("trendx.log",
                        invalid_raise = False,
                        dtype=str, 
                        comments=None,
                        usecols=np.arange(16))

此外，如果要检查匹配的值:

#np.arange(24)
print(df1[df1['SHA-1'].isin(df2['SHA1'])])
                                        SHA-1           VSDT Unnamed: 2
19   0bed7d032d5c51f606befd2f10b94e5c75a6a1e3  WIN32 EXE 7-2        NaN
32   10d6ea590e7e31a396c0fd96cb7413c354ab4b97  WIN32 EXE 7-2        NaN
217  6010a6400d72298fb8e61bff67638da23efd0c81      MSIL 7-18        NaN
231  6614e5097a777cb2192d856c7aa99c73f9104c8a      MSIL 7-18        NaN
296  84a3a384c6d61678d6e335559948cb0e2a32de0b  WIN32 EXE 7-2        NaN
300  85f3b0710776b897208e88460228eab0f2b6df6a  WIN32 EXE 7-2        NaN
340  94571c6299a8bb7a18e374665ff71bcdf7277fc6      MSIL 7-18        NaN
345  96e1e3d135d037696262b20b227b82f6cd3dce44  WIN32 EXE 7-2        NaN
388  acdfacefb1b97d97b896c7af6c47d87f811d7fd9      MSIL 7-18        NaN
408  b61c6e35810f9d506f17874bc1750cd90a57a434  WIN32 EXE 7-2        NaN
503  df88efb7ab874bc024c20c06c0daf8cf34a95897      MSIL 7-18        NaN
511  e1179af687feaeb5b9525df4fbb061d0f424746f      MSIL 7-18        NaN
576  fcb12edabdb2e59916f2f84f204c3e8ec13d1135  WIN32 EXE 7-2        NaN

#np.arange(16)
print(df1[df1['SHA-1'].isin(df2['SHA1'])])
                                        SHA-1           VSDT Unnamed: 2
13   06a60c6018a42b1db22e3bf8620861711401c4bb  WIN32 EXE 7-2        NaN
14   0723a895a5f8b2d5d25b4303e9f04d16551791b6      MSIL 7-18        NaN
19   0bed7d032d5c51f606befd2f10b94e5c75a6a1e3  WIN32 EXE 7-2        NaN
26   0e13d281af08954102e7caf95864ef553c7277bd  Win32 DLL 7-5        NaN
32   10d6ea590e7e31a396c0fd96cb7413c354ab4b97  WIN32 EXE 7-2        NaN
33   113d53cc041fbd25b1004f68493ff1b0d0cd6c1f  WIN32 EXE 7-2        NaN
34   1217b71e04c81f4c50f053793dbe60d91d39668f      MSIL 7-18        NaN
36   134024d595bf9d724213f4303885f4d1e43b7a44  WIN32 EXE 7-2        NaN
37   13a508933a46ca80529145e8470a2147739d0334  WIN32 EXE 7-2        NaN
42   154985ac3d041303e3b5043e2d96e762c6a3ddd1  WIN32 EXE 7-2        NaN
57   1c453871229e8eddd7a965ec140279bb4a618b48  WIN32 EXE 7-2        NaN
61   1df0cfdee270ea0215b3a6a3e9aa2ad8bd820749  WIN32 EXE 7-2        NaN
68   23ef5c7c3384fcff3e9c3f2c647bebce5d1d7558  WIN32 EXE 7-2        NaN
93   2f7e7d2a9a44b03d9525569168bfbb604317be0e  WIN32 EXE 7-2        NaN
106  327891c858ee81955c1945a2787782e958b94ab7  WIN32 EXE 7-2        NaN
111  35be3823638cfb04fbc2f6854faab4bbf1d8a627  WIN32 EXE 7-2        NaN
114  36b13a68ae6c896c68c51ebb89ffd3c484c00457  WIN32 EXE 7-2        NaN
132  3d133c7d15649d607817df5081d85f4397757c67  WIN32 EXE 7-2        NaN
135  3d7aba9ca74e368158b996057a041189b948c9fe  WIN32 EXE 7-2        NaN
142  40a18adc9fdbff2b95997f0175307b76657b037c  WIN32 EXE 7-2        NaN
157  48bde6c540065d04e19f22d2db8f75aca5d3d375  WIN32 EXE 7-2        NaN
158  48e0dcf8325867063619a28f837704ba8d4ce1cb  WIN32 EXE 7-2        NaN
162  4b8a159a69c5ea451d62f9a480e849984687fbf7  WIN32 EXE 7-2        NaN
167  4cb7867c4edaded299199258a7d6062c1c0def89  WIN32 EXE 7-2        NaN
175  501947c29ebbad093881c92ff0c5e4cdce6de64d  WIN32 EXE 7-2        NaN
180  50c8f15c8e94d60f370403a09796f9e44e90b888  WIN32 EXE 7-2        NaN
182  5141321fe113df78d41ec282e54cb49c2cc5125d  WIN32 EXE 7-2        NaN
194  56ef50c4b83c17e03400d129de99869d8ab18c94  WIN32 EXE 7-2        NaN
196  57d4e8300d405655f37ae98667b76c94fc6c400c  WIN32 EXE 7-2        NaN
203  5a339b555ea6c3f7ebe5d8d11890a6d0e738a734   UPX EXE 7-17        NaN
..                                        ...            ...        ...
421  be2adbdea170d0fb7012841d48aab27250a933d2  WIN32 EXE 7-2        NaN
424  bee081ba9c5eae456acfb285cd6a0ae0e289f174  WIN32 EXE 7-2        NaN
438  c4eb16a4dc44b2f2525a6296d234fc272b23454f  WIN32 EXE 7-2        NaN
448  caf937c3c486236c6ec35fdf5bd8dc849ceb02b9  WIN32 EXE 7-2        NaN
453  cc53cdd86d97afbaf321d228b18d7a0ce4e8f9d1      MSIL 7-18        NaN
460  d01a707b473d2599084807e496331c5d78a394f4  WIN32 EXE 7-2        NaN
463  d131e81b35b0514fb66776e84c5f39bf0e637919  WIN32 EXE 7-2        NaN
469  d352365f415f41dced3a6dd4aa4d2c6014c70ed3  WIN32 EXE 7-2        NaN
472  d3e0e1116aa97b51d5cadee2ea50f172c603fa50  WIN32 EXE 7-2        NaN
480  d54caaf59f1294b88f7d5ceb8ae2c0784be2e272  WIN32 EXE 7-2        NaN
483  d6c9b7b47b3576017afbb974ed6b2b5d54787de5  WIN32 EXE 7-2        NaN
487  d913bed0de10c0168bc8ab733f9b5fd20bbd5472  WIN32 EXE 7-2        NaN
489  daed0b94fd0892063f8d4a91dde5e7496eed4e83  WIN32 EXE 7-2        NaN
496  de3261f839ab02e0ee128faffddd3f45e79527dd  WIN32 EXE 7-2        NaN
499  defd56ebf430ac144243e7c8d36d20ea3de10bc4  WIN32 EXE 7-2        NaN
500  df44071358587c90d712b0de78bbca146e3ae223  WIN32 EXE 7-2        NaN
501  df61222fe125e56b02a2cfc797f00ce63904d8df  WIN32 EXE 7-2        NaN
502  df69d622e59945e7baf124b2faf205f00769b978  WIN32 EXE 7-2        NaN
503  df88efb7ab874bc024c20c06c0daf8cf34a95897      MSIL 7-18        NaN
505  e043b9d5410458342ff7a911de699cc0aa453610  WIN32 EXE 7-2        NaN
508  e0ee714a5bd67fc6cc68f8419ae336db44fc8a8e  WIN32 EXE 7-2        NaN
511  e1179af687feaeb5b9525df4fbb061d0f424746f      MSIL 7-18        NaN
527  e7e4a72fb5924051a41155044f03f55aaa304266  WIN32 EXE 7-2        NaN
529  e8bc0782cec91da0044eb275db69f79542c336c1  WIN32 EXE 7-2        NaN
542  ec554c9d8c10c1dddc1a38418c627c344991f640  WIN32 EXE 7-2        NaN
544  eca602bca855cac979a99b44d3ae033daa43bc39  WIN32 EXE 7-2        NaN
547  ed66e83ae790873fd92fef146a2b70e5597792ee  WIN32 EXE 7-2        NaN
548  ed6c6a9e55e501520b476087cb5eeaf820b89194      MSIL 7-18        NaN
576  fcb12edabdb2e59916f2f84f204c3e8ec13d1135  WIN32 EXE 7-2        NaN
578  fced05723f49b6d0836e065a436e8c3b8df2bc12  WIN32 EXE 7-2        NaN

[97 rows x 3 columns]

所有代码:

#Log data into dataframe using genfromtxt
logdata = np.genfromtxt("trendx.log",invalid_raise = False,dtype=str, comments=None,usecols=np.arange(16))
logframe = pd.DataFrame(logdata)
#print (logframe.head())

#Dataframe trimmed to use only SHA1, PRG and IP
df2=(logframe[[10,14,15]]).rename(columns={10:'SHA-1', 14: 'PRG',15:'IP'})
#print (df2.head())

#sha1_vsdt data into dataframe using read_csv
df1=pd.read_csv("sha1_vsdt.csv",delimiter=",",error_bad_lines=False,engine = 'python',quoting=3)
#Using merge to compare the two CSV

df = pd.merge(df1, df2, on='SHA-1', how='left').fillna('undetected')

print(df[['SHA-1','VSDT','PRG','IP']])

                                                 SHA-1  \
0             0191a23ee122bdb0c69008971e365ec530bf03f5   
1             02b809d4edee752d9286677ea30e8a76114aa324   
2             0349e0101d8458b6d05860fbee2b4a6d7fa2038d   
3             035a7afca8b72cf1c05f6062814836ee31091559   
4             042065bec5a655f3daec1442addf5acb8f1aa824   
5             04939e040d9e85f84d2e2eb28343d94a50ed46ac   
6             04a1876724b53a016cd9e9c93735985938c91fa4   
7             06109df23f7d5deadf0b2c158af1f71c2997d245   
8             06194c240c12c51b55d2961ae287fd9628e05751   
9             0665de1ad83715cc6e68d00ed700c469944a5925   
10            067b448f4c9782489e5ff60c31c62b7059e500b2   
11            0688e6966b0e4a1f58d2f3de48f960fce5b42292   
12            0689f6f99d10dd8bf396f2d2c73ce9dcb6dcad23   
13            06a60c6018a42b1db22e3bf8620861711401c4bb   
14            0723a895a5f8b2d5d25b4303e9f04d16551791b6   
15            07344621cf4480c430f8931af2b2b056775af7e3   
16            07831df482f1a34310fc4f5a092c333eeaff4380   
17            08386105057cd5867480095696a5ca6701fdb8ad   
18            0ad5f62b4ec10397b7d13433a8dc794dc6d4f273   
19            0bed7d032d5c51f606befd2f10b94e5c75a6a1e3   
20            0c3f8d2cce9e7a6e5604b8d0c9fbe1ff6fd5cebb   
21            0c793b4f4e0be7f24f93786d7d4a719a7a002a0d   
22            0c7c2b2d05a5c712f4b9302b82fb54007210937f   
23            0d03da55b246252fb5b440a23943426bda965bcd   
24            0d592f948a4f7bfa95c7cb09faf067ce9fbc9375   
25            0df65d8a57c8349e044f98deda17d70d0c4f926a   
26            0e13d281af08954102e7caf95864ef553c7277bd   
27            0ede12d9c17564e803f51de4d279e84623c5a8a6   
28            0fc4f3a30684bb17cbcbf4e3def2ac3528a2f04c   
29            0fcb475fcadd8d8e3b8dd5f4376feda48c73fd24   
..                                                 ...   
553           ef90b17c18c3c5960726964cff12b6d6ef22f3f4   
554           effbed4e7e619009def1c4322f68092eb9cc197f   
555           f081c8a737f87167fef83d03405c1fbe55a46986   
556           f1304ad198045ebb93e70252f0dda9d68acd83f1   
557           f14762b5ce92f2713c584140d694ce25f7beb9c2   
558           f187959d6afa483d18c69b9e334575781009cd31   
559           f1ae32a92f89f54e542973a98eb3dcbe05fe9c58   
560           f28217b5928e4d2fbbc5ca45bd815b1c3963bed2   
561           f36687584c4bc38f2aed5511930b50eea378c1bf   
562           f4846b38f52805ffa2d0ae392df05bbeb8fee2b5   
563           f4b8b762feb426de46a0d19b86f31173e0e77c2e   
564           f4d0cc44a8018c807b9b1865ce2dd70f027d2ceb   
565           f4fcbbdf8c797c96dd1a3e76baf666c319f52aa8   
566           f6c9b393b5148e45138f724cebf5b1e2fd8d9bc7   
567           f8910d7869be647d2ec6c49ddf6fef49ed0f09d0   
568           f90c38a3d623ea47b129b386d841614d9a290f0a   
569           f99c069d5ababc7001aa46a494a0400a913a109c   
570           f9d2c6e2438fc4571f7ea4f639b2950ddd1307e5   
571           fa2229ef95b9e45e881ac27004c2a90f6c6e0947   
572           fac66887402b4ac4a39696f3f8830a6ec34585be   
573           fb2086d390c1755b53580013c727398d9fb5c01b   
574           fb59aa51fec66f8caf409b1ca2b80e7fdaf33c61   
575  fc -...   
576           fcb12edabdb2e59916f2f84f204c3e8ec13d1135   
577           fcbbfeb67cd2902de545fb159b0eed7343aeb502   
578           fced05723f49b6d0836e065a436e8c3b8df2bc12   
579           fd1cada68f4a9452275d292fe4b9f76a4bd8bd8b   
580           fe5babc1e4f11e205457f2ec616f117fd4f4e326   
581           fe8c341de79168a1254154f4e4403857c6e79c46   
582           fe91021461e48fe82449d2ad73bcc66f6c508152   

                                           VSDT           PRG             IP  
0                                   MIME 6010-0    undetected     undetected  
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[583 rows x 4 columns]

关于python - 使用python检测日志文件中是否存在csv列中的字符串？，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/52641266/

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python - 使用python检测日志文件中是否存在csv列中的字符串？