├── images └── ACCD.png ├── Hunting C2 Beaconing at Scale in the Modern Age.pdf ├── README.md ├── .gitignore ├── LICENSE ├── sample-data └── sample_beacon_dataset.csv └── AC&CD.ipynb /images/ACCD.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Cyb3r-Monk/ACCD/HEAD/images/ACCD.png -------------------------------------------------------------------------------- /Hunting C2 Beaconing at Scale in the Modern Age.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Cyb3r-Monk/ACCD/HEAD/Hunting C2 Beaconing at Scale in the Modern Age.pdf -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # AC&CD: Active C&C Detector 2 | 3 | ![ACCD](./images/ACCD.png) 4 | 5 | AC&CD is a tool for detecting malicious beaconing activity. Current detection strategies rely on beacons using a fixed sleep and jitter configuration. However, in real attacks, the sleep and jitter configuration is often dynamic, meaning that attackers change the sleep and jitter configuration on the fly according to their needs, such as SOCKS tunneling, off-business hours, etc. AC&CD uses a different approach and finds potential beaconing activity where there is a certain period of time that the attackers are active on the keyboard and executing commands on the victim machine. -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | *$py.class 5 | 6 | # C extensions 7 | *.so 8 | 9 | # Distribution / packaging 10 | .Python 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | .eggs/ 17 | lib/ 18 | lib64/ 19 | parts/ 20 | sdist/ 21 | var/ 22 | wheels/ 23 | share/python-wheels/ 24 | *.egg-info/ 25 | .installed.cfg 26 | *.egg 27 | MANIFEST 28 | 29 | # PyInstaller 30 | # Usually these files are written by a python script from a template 31 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 32 | *.manifest 33 | *.spec 34 | 35 | # Installer logs 36 | pip-log.txt 37 | pip-delete-this-directory.txt 38 | 39 | # Unit test / coverage reports 40 | htmlcov/ 41 | .tox/ 42 | .nox/ 43 | .coverage 44 | .coverage.* 45 | .cache 46 | nosetests.xml 47 | coverage.xml 48 | *.cover 49 | *.py,cover 50 | .hypothesis/ 51 | .pytest_cache/ 52 | cover/ 53 | 54 | # Translations 55 | *.mo 56 | *.pot 57 | 58 | # Django stuff: 59 | *.log 60 | local_settings.py 61 | db.sqlite3 62 | db.sqlite3-journal 63 | 64 | # Flask stuff: 65 | instance/ 66 | .webassets-cache 67 | 68 | # Scrapy stuff: 69 | .scrapy 70 | 71 | # Sphinx documentation 72 | docs/_build/ 73 | 74 | # PyBuilder 75 | .pybuilder/ 76 | target/ 77 | 78 | # Jupyter Notebook 79 | .ipynb_checkpoints 80 | .DS_Store 81 | 82 | # IPython 83 | profile_default/ 84 | ipython_config.py 85 | 86 | # pyenv 87 | # For a library or package, you might want to ignore these files since the code is 88 | # intended to run in multiple environments; otherwise, check them in: 89 | # .python-version 90 | 91 | # pipenv 92 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. 93 | # However, in case of collaboration, if having platform-specific dependencies or dependencies 94 | # having no cross-platform support, pipenv may install dependencies that don't work, or not 95 | # install all needed dependencies. 96 | #Pipfile.lock 97 | 98 | # poetry 99 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. 100 | # This is especially recommended for binary packages to ensure reproducibility, and is more 101 | # commonly ignored for libraries. 102 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control 103 | #poetry.lock 104 | 105 | # pdm 106 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. 107 | #pdm.lock 108 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it 109 | # in version control. 110 | # https://pdm.fming.dev/#use-with-ide 111 | .pdm.toml 112 | 113 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm 114 | __pypackages__/ 115 | 116 | # Celery stuff 117 | celerybeat-schedule 118 | celerybeat.pid 119 | 120 | # SageMath parsed files 121 | *.sage.py 122 | 123 | # Environments 124 | .env 125 | .venv 126 | env/ 127 | venv/ 128 | ENV/ 129 | env.bak/ 130 | venv.bak/ 131 | 132 | # Spyder project settings 133 | .spyderproject 134 | .spyproject 135 | 136 | # Rope project settings 137 | .ropeproject 138 | 139 | # mkdocs documentation 140 | /site 141 | 142 | # mypy 143 | .mypy_cache/ 144 | .dmypy.json 145 | dmypy.json 146 | 147 | # Pyre type checker 148 | .pyre/ 149 | 150 | # pytype static type analyzer 151 | .pytype/ 152 | 153 | # Cython debug symbols 154 | cython_debug/ 155 | 156 | # PyCharm 157 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can 158 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore 159 | # and can be added to the global gitignore or merged into this file. 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No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. 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Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | -------------------------------------------------------------------------------- /sample-data/sample_beacon_dataset.csv: -------------------------------------------------------------------------------- 1 | Timestamp,DestinationHostName,DestinationPort,DestinationIP,ReceivedBytes,SentBytes,Protocol,RequestMethod,SourceIP 2 | "7/14/2023, 1:11:02.921 AM",www.amazon.com,,1.2.3.4.5,257,279,,GET,10.10.25.52 3 | "7/14/2023, 1:10:10.909 AM",www.amazon.com,,1.2.3.4.5,257,279,,GET,10.10.25.52 4 | "7/14/2023, 1:09:13.932 AM",www.amazon.com,,1.2.3.4.5,257,279,,GET,10.10.25.52 5 | "7/14/2023, 1:08:06.920 AM",www.amazon.com,,1.2.3.4.5,257,279,,GET,10.10.25.52 6 | "7/14/2023, 1:06:58.916 AM",www.amazon.com,,1.2.3.4.5,257,279,,GET,10.10.25.52 7 | "7/14/2023, 1:05:52.003 AM",www.amazon.com,,1.2.3.4.5,257,279,,GET,10.10.25.52 8 | "7/14/2023, 1:04:42.903 AM",www.amazon.com,,1.2.3.4.5,257,279,,GET,10.10.25.52 9 | "7/14/2023, 1:03:40.909 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"metadata": {}, 6 | "source": [ 7 | "# AC&CD: Active C&C Detector\n", 8 | "![ACCD](./images/ACCD.png)" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "metadata": {}, 14 | "source": [ 15 | "# How AC&CD works\n", 16 | "\n", 17 | "AC&CD analyzes time delta size distribution and data size(sent and received bytes) distribution of a traffic between the same source and the destination and produces an overall score.\n", 18 | "\n", 19 | "## Time delta distribution scoring\n", 20 | "AC&CD calculates a score based on time delta distribution. \n", 21 | "\n", 22 | "In a real attack, attackers change the sleep and jitter configuration on the fly according to their needs such as SOCKS tunneling, off-business hours, etc. The change in the sleep and jitter makes the traffic not look like beaconing and causes False Negatives. To solve the issue, AC&CD analyzes the certain part of the time delta set where higher sleep values can't cause false negatives. The certain part of the time delta set is roughly the first 60% of the time delta set. Therefore, AC&CD calculates the time delta distribution score from the first 60% of the time delta set by using 15th, 30th and 45th percentiles of the whole dataset.\n", 23 | "\n", 24 | "### Median Absolute Deviation of time delta\n", 25 | "When jitter is used in beaconing, the time delta set is expected to have some dispersion around the median of the time delta set. Median Absolute Deviation(MAD) is used to check dispersion. Dividing the MAD by the median of the time delta set gives a normalized dispersion value which is the jitter in the dataset. The normalized dispersion value is used to calculate time delta score.\n", 26 | "\n", 27 | "\n", 28 | "## Data size scoring\n", 29 | "AC&CD calculates an overal data size score based on data size(sent and received bytes) distribution. \n", 30 | "\n", 31 | "### Sent Bytes Distribution\n", 32 | "AC&CD calculates the sent bytes distribution score by using 15th, 30th and 45th percentiles of the whole dataset as in time delta distribution scoring.\n", 33 | "\n", 34 | "### Received Bytes Distribution\n", 35 | "In real attacks, attackers send command/tools to the beacon. Therefore, there has to be some communication with higher received bytes. AC&CD checks the maximum received bytes in the dataset and if it's less than 20.000, it reduces the data size score. \n", 36 | "\n", 37 | "## Skewness\n", 38 | "Jitter usage in beaconing causes random distribution of the sleep values and does not guarantee uniform distribution. Therefore, AC&CD doesn't use skewness in the scoring.\n", 39 | "\n", 40 | "---" 41 | ] 42 | }, 43 | { 44 | "cell_type": "markdown", 45 | "metadata": {}, 46 | "source": [ 47 | "# Implementing the algorithm using Jupyter notebook" 48 | ] 49 | }, 50 | { 51 | "cell_type": "markdown", 52 | "metadata": {}, 53 | "source": [ 54 | "## Steps: \n", 55 | "0. Prepare data\n", 56 | "1. Group connections between the same hosts and aggregate timestamps into a list\n", 57 | "2. Calculate connection count and remove short sessions\n", 58 | "3. Sort the connection timestamp ascending\n", 59 | "4. Calculate time deltas\n", 60 | "5. Generate variables required for score calculation (time delta and data size)\n", 61 | "6. Calculate the score (time delta, data size, overall score)" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 68, 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "import pandas as pd\n", 71 | "import numpy as np\n", 72 | "import warnings\n", 73 | "# Disable warnings\n", 74 | "warnings.filterwarnings('ignore')\n", 75 | "warnings.simplefilter('ignore')" 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": {}, 81 | "source": [ 82 | "## Preparing the Data" 83 | ] 84 | }, 85 | { 86 | "cell_type": "markdown", 87 | "metadata": {}, 88 | "source": [ 89 | "### Loading the data" 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 69, 95 | "metadata": {}, 96 | "outputs": [ 97 | { 98 | "data": { 99 | "text/html": [ 100 | "
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TimestampDestinationHostNameDestinationPortDestinationIPReceivedBytesSentBytesProtocolRequestMethodSourceIP
07/14/2023, 1:11:02.921 AMwww.amazon.comNaN1.2.3.4.5257279NaNGET10.10.25.52
17/14/2023, 1:10:10.909 AMwww.amazon.comNaN1.2.3.4.5257279NaNGET10.10.25.52
27/14/2023, 1:09:13.932 AMwww.amazon.comNaN1.2.3.4.5257279NaNGET10.10.25.52
37/14/2023, 1:08:06.920 AMwww.amazon.comNaN1.2.3.4.5257279NaNGET10.10.25.52
47/14/2023, 1:06:58.916 AMwww.amazon.comNaN1.2.3.4.5257279NaNGET10.10.25.52
\n", 192 | "
" 193 | ], 194 | "text/plain": [ 195 | " Timestamp DestinationHostName DestinationPort \\\n", 196 | "0 7/14/2023, 1:11:02.921 AM www.amazon.com NaN \n", 197 | "1 7/14/2023, 1:10:10.909 AM www.amazon.com NaN \n", 198 | "2 7/14/2023, 1:09:13.932 AM www.amazon.com NaN \n", 199 | "3 7/14/2023, 1:08:06.920 AM www.amazon.com NaN \n", 200 | "4 7/14/2023, 1:06:58.916 AM www.amazon.com NaN \n", 201 | "\n", 202 | " DestinationIP ReceivedBytes SentBytes Protocol RequestMethod SourceIP \n", 203 | "0 1.2.3.4.5 257 279 NaN GET 10.10.25.52 \n", 204 | "1 1.2.3.4.5 257 279 NaN GET 10.10.25.52 \n", 205 | "2 1.2.3.4.5 257 279 NaN GET 10.10.25.52 \n", 206 | "3 1.2.3.4.5 257 279 NaN GET 10.10.25.52 \n", 207 | "4 1.2.3.4.5 257 279 NaN GET 10.10.25.52 " 208 | ] 209 | }, 210 | "execution_count": 69, 211 | "metadata": {}, 212 | "output_type": "execute_result" 213 | } 214 | ], 215 | "source": [ 216 | "http_df = pd.read_csv('./sample-data/sample_beacon_dataset.csv')\n", 217 | "http_df.head()" 218 | ] 219 | }, 220 | { 221 | "cell_type": "code", 222 | "execution_count": 70, 223 | "metadata": {}, 224 | "outputs": [], 225 | "source": [ 226 | "# assign field/column names to variables\n", 227 | "f_timestamp = 'Timestamp'\n", 228 | "f_src_ip = 'SourceIP'\n", 229 | "f_dst_ip = 'DestinationIP'\n", 230 | "f_dst_host = 'DestinationHostName'\n", 231 | "f_dst_port = 'DestinationPort'\n", 232 | "f_http_method = 'RequestMethod'\n", 233 | "f_sent_bytes = 'SentBytes'\n", 234 | "f_received_bytes = 'ReceivedBytes'\n", 235 | "\n", 236 | "columns_to_filter = [f_timestamp, f_src_ip, f_dst_ip, f_dst_host, f_dst_port, f_http_method, f_sent_bytes, f_received_bytes]\n", 237 | "columns_to_groupby = [f_src_ip, f_dst_host, f_http_method]\n", 238 | "# columns to display after the analysis\n", 239 | "columns_to_display = ['Score', 'tsScore', 'dsScore', 'conn_count', 'min_execution_bytes', 'dsResponseMax', 'destination_prevalence', f_src_ip, f_dst_ip, f_dst_host, f_http_method, f_dst_port, f_sent_bytes, f_received_bytes, 'deltas']" 240 | ] 241 | }, 242 | { 243 | "cell_type": "markdown", 244 | "metadata": {}, 245 | "source": [ 246 | "### Filtering Required Columns\n", 247 | "```\n", 248 | "df.loc[first_row_index:last_row_index , ['column1', 'column3']]\n", 249 | "```" 250 | ] 251 | }, 252 | { 253 | "cell_type": "code", 254 | "execution_count": 71, 255 | "metadata": {}, 256 | "outputs": [ 257 | { 258 | "name": "stdout", 259 | "output_type": "stream", 260 | "text": [ 261 | "\n", 262 | "RangeIndex: 876 entries, 0 to 875\n", 263 | "Data columns (total 8 columns):\n", 264 | " # Column Non-Null Count Dtype \n", 265 | "--- ------ -------------- ----- \n", 266 | " 0 Timestamp 876 non-null object \n", 267 | " 1 SourceIP 876 non-null object \n", 268 | " 2 DestinationIP 876 non-null object \n", 269 | " 3 DestinationHostName 876 non-null object \n", 270 | " 4 DestinationPort 0 non-null float64\n", 271 | " 5 RequestMethod 876 non-null object \n", 272 | " 6 SentBytes 876 non-null int64 \n", 273 | " 7 ReceivedBytes 876 non-null int64 \n", 274 | "dtypes: float64(1), int64(2), object(5)\n", 275 | "memory usage: 54.9+ KB\n" 276 | ] 277 | } 278 | ], 279 | "source": [ 280 | "# get all rows and only the required colums\n", 281 | "http_df = http_df.loc[:,columns_to_filter]\n", 282 | "http_df.info()" 283 | ] 284 | }, 285 | { 286 | "cell_type": "markdown", 287 | "metadata": {}, 288 | "source": [ 289 | "### Fixing the Timestamp Column Type" 290 | ] 291 | }, 292 | { 293 | "cell_type": "code", 294 | "execution_count": 72, 295 | "metadata": {}, 296 | "outputs": [ 297 | { 298 | "data": { 299 | "text/html": [ 300 | "
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TimestampSourceIPDestinationIPDestinationHostNameDestinationPortRequestMethodSentBytesReceivedBytes
02023-07-14 01:11:02.92110.10.25.521.2.3.4.5www.amazon.comNaNGET279257
12023-07-14 01:10:10.90910.10.25.521.2.3.4.5www.amazon.comNaNGET279257
\n", 353 | "
" 354 | ], 355 | "text/plain": [ 356 | " Timestamp SourceIP DestinationIP DestinationHostName \\\n", 357 | "0 2023-07-14 01:11:02.921 10.10.25.52 1.2.3.4.5 www.amazon.com \n", 358 | "1 2023-07-14 01:10:10.909 10.10.25.52 1.2.3.4.5 www.amazon.com \n", 359 | "\n", 360 | " DestinationPort RequestMethod SentBytes ReceivedBytes \n", 361 | "0 NaN GET 279 257 \n", 362 | "1 NaN GET 279 257 " 363 | ] 364 | }, 365 | "execution_count": 72, 366 | "metadata": {}, 367 | "output_type": "execute_result" 368 | } 369 | ], 370 | "source": [ 371 | "# unit conversion should match with the format (try ns, ms, or s)\n", 372 | "http_df[f_timestamp] = pd.to_datetime(http_df[f_timestamp], unit='ns')\n", 373 | "http_df.head(2)" 374 | ] 375 | }, 376 | { 377 | "cell_type": "markdown", 378 | "metadata": {}, 379 | "source": [ 380 | "## Analysing the Data\n", 381 | "Now the data is ready for statistical analysis. " 382 | ] 383 | }, 384 | { 385 | "cell_type": "markdown", 386 | "metadata": {}, 387 | "source": [ 388 | "### Grouping the Connections\n", 389 | "We can group the connections between the same hosts and aggregate the timestamps into a list. This way, we can use **pd.Series().diff** to easily calculate time deltas. \n", 390 | "First, let's group the traffic and aggregate." 391 | ] 392 | }, 393 | { 394 | "cell_type": "code", 395 | "execution_count": 73, 396 | "metadata": {}, 397 | "outputs": [ 398 | { 399 | "data": { 400 | "text/html": [ 401 | "
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TimestampDestinationIPDestinationPortSentBytesReceivedBytes
SourceIPDestinationHostNameRequestMethod
10.10.25.52www.amazon.comGET[2023-07-14 01:11:02.921000, 2023-07-14 01:10:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[279, 279, 279, 279, 279, 279, 279, 279, 279, ...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...
POST[2023-07-14 01:03:40.909000, 2023-07-14 01:02:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[595, 425214, 595, 5116, 595, 992910, 1870023,...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...
\n", 459 | "
" 460 | ], 461 | "text/plain": [ 462 | " Timestamp \\\n", 463 | "SourceIP DestinationHostName RequestMethod \n", 464 | "10.10.25.52 www.amazon.com GET [2023-07-14 01:11:02.921000, 2023-07-14 01:10:... \n", 465 | " POST [2023-07-14 01:03:40.909000, 2023-07-14 01:02:... \n", 466 | "\n", 467 | " DestinationIP \\\n", 468 | "SourceIP DestinationHostName RequestMethod \n", 469 | "10.10.25.52 www.amazon.com GET [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 470 | " POST [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 471 | "\n", 472 | " DestinationPort \\\n", 473 | "SourceIP DestinationHostName RequestMethod \n", 474 | "10.10.25.52 www.amazon.com GET [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 475 | " POST [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 476 | "\n", 477 | " SentBytes \\\n", 478 | "SourceIP DestinationHostName RequestMethod \n", 479 | "10.10.25.52 www.amazon.com GET [279, 279, 279, 279, 279, 279, 279, 279, 279, ... \n", 480 | " POST [595, 425214, 595, 5116, 595, 992910, 1870023,... \n", 481 | "\n", 482 | " ReceivedBytes \n", 483 | "SourceIP DestinationHostName RequestMethod \n", 484 | "10.10.25.52 www.amazon.com GET [257, 257, 257, 257, 257, 257, 257, 257, 257, ... \n", 485 | " POST [257, 257, 257, 257, 257, 257, 257, 257, 257, ... " 486 | ] 487 | }, 488 | "execution_count": 73, 489 | "metadata": {}, 490 | "output_type": "execute_result" 491 | } 492 | ], 493 | "source": [ 494 | "# If you have a large dateset, using groupby and aggregate(list) might be slow. Consider using dask\n", 495 | "http_df = http_df.groupby(columns_to_groupby).agg(list)\n", 496 | "http_df.head(5)" 497 | ] 498 | }, 499 | { 500 | "cell_type": "markdown", 501 | "metadata": {}, 502 | "source": [ 503 | "#### Reseting the index\n", 504 | "We need to reset the index to use all the columns." 505 | ] 506 | }, 507 | { 508 | "cell_type": "code", 509 | "execution_count": 74, 510 | "metadata": {}, 511 | "outputs": [ 512 | { 513 | "data": { 514 | "text/html": [ 515 | "
\n", 516 | "\n", 529 | "\n", 530 | " \n", 531 | " \n", 532 | " \n", 533 | " \n", 534 | " \n", 535 | " \n", 536 | " \n", 537 | " \n", 538 | " \n", 539 | " \n", 540 | " \n", 541 | " \n", 542 | " \n", 543 | " \n", 544 | " \n", 545 | " \n", 546 | " \n", 547 | " \n", 548 | " \n", 549 | " \n", 550 | " \n", 551 | " \n", 552 | " \n", 553 | " \n", 554 | " \n", 555 | " \n", 556 | " \n", 557 | " \n", 558 | " \n", 559 | " \n", 560 | " \n", 561 | " \n", 562 | " \n", 563 | " \n", 564 | " \n", 565 | " \n", 566 | " \n", 567 | "
SourceIPDestinationHostNameRequestMethodTimestampDestinationIPDestinationPortSentBytesReceivedBytes
010.10.25.52www.amazon.comGET[2023-07-14 01:11:02.921000, 2023-07-14 01:10:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[279, 279, 279, 279, 279, 279, 279, 279, 279, ...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...
110.10.25.52www.amazon.comPOST[2023-07-14 01:03:40.909000, 2023-07-14 01:02:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[595, 425214, 595, 5116, 595, 992910, 1870023,...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...
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" 569 | ], 570 | "text/plain": [ 571 | " SourceIP DestinationHostName RequestMethod \\\n", 572 | "0 10.10.25.52 www.amazon.com GET \n", 573 | "1 10.10.25.52 www.amazon.com POST \n", 574 | "\n", 575 | " Timestamp \\\n", 576 | "0 [2023-07-14 01:11:02.921000, 2023-07-14 01:10:... \n", 577 | "1 [2023-07-14 01:03:40.909000, 2023-07-14 01:02:... \n", 578 | "\n", 579 | " DestinationIP \\\n", 580 | "0 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 581 | "1 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 582 | "\n", 583 | " DestinationPort \\\n", 584 | "0 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 585 | "1 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 586 | "\n", 587 | " SentBytes \\\n", 588 | "0 [279, 279, 279, 279, 279, 279, 279, 279, 279, ... \n", 589 | "1 [595, 425214, 595, 5116, 595, 992910, 1870023,... \n", 590 | "\n", 591 | " ReceivedBytes \n", 592 | "0 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... \n", 593 | "1 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... " 594 | ] 595 | }, 596 | "execution_count": 74, 597 | "metadata": {}, 598 | "output_type": "execute_result" 599 | } 600 | ], 601 | "source": [ 602 | "http_df.reset_index(inplace=True)\n", 603 | "http_df.head(2)" 604 | ] 605 | }, 606 | { 607 | "cell_type": "markdown", 608 | "metadata": {}, 609 | "source": [ 610 | "### Calculating Connection Count\n", 611 | "Length of the timestamp list ('ts') = connection count (since we grouped the traffic and aggregated the timestamp into a list) " 612 | ] 613 | }, 614 | { 615 | "cell_type": "code", 616 | "execution_count": 75, 617 | "metadata": {}, 618 | "outputs": [ 619 | { 620 | "data": { 621 | "text/html": [ 622 | "
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SourceIPDestinationHostNameRequestMethodTimestampDestinationIPDestinationPortSentBytesReceivedBytesconn_count
010.10.25.52www.amazon.comGET[2023-07-14 01:11:02.921000, 2023-07-14 01:10:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[279, 279, 279, 279, 279, 279, 279, 279, 279, ...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...848
110.10.25.52www.amazon.comPOST[2023-07-14 01:03:40.909000, 2023-07-14 01:02:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[595, 425214, 595, 5116, 595, 992910, 1870023,...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...28
\n", 678 | "
" 679 | ], 680 | "text/plain": [ 681 | " SourceIP DestinationHostName RequestMethod \\\n", 682 | "0 10.10.25.52 www.amazon.com GET \n", 683 | "1 10.10.25.52 www.amazon.com POST \n", 684 | "\n", 685 | " Timestamp \\\n", 686 | "0 [2023-07-14 01:11:02.921000, 2023-07-14 01:10:... \n", 687 | "1 [2023-07-14 01:03:40.909000, 2023-07-14 01:02:... \n", 688 | "\n", 689 | " DestinationIP \\\n", 690 | "0 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 691 | "1 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 692 | "\n", 693 | " DestinationPort \\\n", 694 | "0 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 695 | "1 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 696 | "\n", 697 | " SentBytes \\\n", 698 | "0 [279, 279, 279, 279, 279, 279, 279, 279, 279, ... \n", 699 | "1 [595, 425214, 595, 5116, 595, 992910, 1870023,... \n", 700 | "\n", 701 | " ReceivedBytes conn_count \n", 702 | "0 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... 848 \n", 703 | "1 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... 28 " 704 | ] 705 | }, 706 | "execution_count": 75, 707 | "metadata": {}, 708 | "output_type": "execute_result" 709 | } 710 | ], 711 | "source": [ 712 | "# create a new column 'conn_count', and for each row in the 'ts' column, apply a function and assign the returned value to the 'conn_count' column\n", 713 | "http_df['conn_count'] = http_df[f_timestamp].apply(lambda x: len(x))\n", 714 | "http_df.head(2)" 715 | ] 716 | }, 717 | { 718 | "cell_type": "markdown", 719 | "metadata": {}, 720 | "source": [ 721 | "### Remove short sessions\n", 722 | "Filter out traffic where the connection count is quite small" 723 | ] 724 | }, 725 | { 726 | "cell_type": "code", 727 | "execution_count": 76, 728 | "metadata": {}, 729 | "outputs": [ 730 | { 731 | "data": { 732 | "text/plain": [ 733 | "(2, 9)" 734 | ] 735 | }, 736 | "execution_count": 76, 737 | "metadata": {}, 738 | "output_type": "execute_result" 739 | } 740 | ], 741 | "source": [ 742 | "http_df = http_df.loc[http_df['conn_count'] >= 24]\n", 743 | "http_df.shape" 744 | ] 745 | }, 746 | { 747 | "cell_type": "markdown", 748 | "metadata": {}, 749 | "source": [ 750 | "#### Sorting the Timestamps\n", 751 | "Apply a lambda function to each row on the specified columns." 752 | ] 753 | }, 754 | { 755 | "cell_type": "code", 756 | "execution_count": 77, 757 | "metadata": {}, 758 | "outputs": [ 759 | { 760 | "data": { 761 | "text/html": [ 762 | "
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SourceIPDestinationHostNameRequestMethodTimestampDestinationIPDestinationPortSentBytesReceivedBytesconn_count
010.10.25.52www.amazon.comGET[2023-07-13 11:05:59.388000, 2023-07-13 11:06:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[279, 279, 279, 279, 279, 279, 279, 279, 279, ...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...848
110.10.25.52www.amazon.comPOST[2023-07-13 11:10:33.076000, 2023-07-13 11:10:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[595, 425214, 595, 5116, 595, 992910, 1870023,...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...28
\n", 818 | "
" 819 | ], 820 | "text/plain": [ 821 | " SourceIP DestinationHostName RequestMethod \\\n", 822 | "0 10.10.25.52 www.amazon.com GET \n", 823 | "1 10.10.25.52 www.amazon.com POST \n", 824 | "\n", 825 | " Timestamp \\\n", 826 | "0 [2023-07-13 11:05:59.388000, 2023-07-13 11:06:... \n", 827 | "1 [2023-07-13 11:10:33.076000, 2023-07-13 11:10:... \n", 828 | "\n", 829 | " DestinationIP \\\n", 830 | "0 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 831 | "1 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 832 | "\n", 833 | " DestinationPort \\\n", 834 | "0 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 835 | "1 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 836 | "\n", 837 | " SentBytes \\\n", 838 | "0 [279, 279, 279, 279, 279, 279, 279, 279, 279, ... \n", 839 | "1 [595, 425214, 595, 5116, 595, 992910, 1870023,... \n", 840 | "\n", 841 | " ReceivedBytes conn_count \n", 842 | "0 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... 848 \n", 843 | "1 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... 28 " 844 | ] 845 | }, 846 | "execution_count": 77, 847 | "metadata": {}, 848 | "output_type": "execute_result" 849 | } 850 | ], 851 | "source": [ 852 | "http_df[f_timestamp] = http_df[f_timestamp].apply(lambda x: sorted(x))\n", 853 | "http_df.head(2)" 854 | ] 855 | }, 856 | { 857 | "cell_type": "markdown", 858 | "metadata": {}, 859 | "source": [ 860 | "### Calculating Time Delta\n", 861 | "Apply **pd.Series.diff()** to the 'ts' column, which is sorted, and assign the resulting list into a new column" 862 | ] 863 | }, 864 | { 865 | "cell_type": "code", 866 | "execution_count": 78, 867 | "metadata": {}, 868 | "outputs": [ 869 | { 870 | "data": { 871 | "text/html": [ 872 | "
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SourceIPDestinationHostNameRequestMethodTimestampDestinationIPDestinationPortSentBytesReceivedBytesconn_countdeltas
010.10.25.52www.amazon.comGET[2023-07-13 11:05:59.388000, 2023-07-13 11:06:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[279, 279, 279, 279, 279, 279, 279, 279, 279, ...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...848[10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,...
110.10.25.52www.amazon.comPOST[2023-07-13 11:10:33.076000, 2023-07-13 11:10:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[595, 425214, 595, 5116, 595, 992910, 1870023,...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...28[0.0, 0.0, 0.0, 0.0, 4100.0, 123.0, 458.0, 60....
\n", 931 | "
" 932 | ], 933 | "text/plain": [ 934 | " SourceIP DestinationHostName RequestMethod \\\n", 935 | "0 10.10.25.52 www.amazon.com GET \n", 936 | "1 10.10.25.52 www.amazon.com POST \n", 937 | "\n", 938 | " Timestamp \\\n", 939 | "0 [2023-07-13 11:05:59.388000, 2023-07-13 11:06:... \n", 940 | "1 [2023-07-13 11:10:33.076000, 2023-07-13 11:10:... \n", 941 | "\n", 942 | " DestinationIP \\\n", 943 | "0 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 944 | "1 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 945 | "\n", 946 | " DestinationPort \\\n", 947 | "0 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 948 | "1 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 949 | "\n", 950 | " SentBytes \\\n", 951 | "0 [279, 279, 279, 279, 279, 279, 279, 279, 279, ... \n", 952 | "1 [595, 425214, 595, 5116, 595, 992910, 1870023,... \n", 953 | "\n", 954 | " ReceivedBytes conn_count \\\n", 955 | "0 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... 848 \n", 956 | "1 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... 28 \n", 957 | "\n", 958 | " deltas \n", 959 | "0 [10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,... \n", 960 | "1 [0.0, 0.0, 0.0, 0.0, 4100.0, 123.0, 458.0, 60.... " 961 | ] 962 | }, 963 | "execution_count": 78, 964 | "metadata": {}, 965 | "output_type": "execute_result" 966 | } 967 | ], 968 | "source": [ 969 | "# Convert list into a Series object, get time delta, convert the result back into a list and assign it to the 'deltas' column\n", 970 | "http_df['deltas'] = http_df[f_timestamp].apply(lambda x: pd.Series(x).diff().dt.seconds.dropna().tolist())\n", 971 | "http_df.head(2)" 972 | ] 973 | }, 974 | { 975 | "cell_type": "markdown", 976 | "metadata": {}, 977 | "source": [ 978 | "### Generate variables\n", 979 | "We need to generate the required variables to analyze time delta distribution, median absolute deviation, and connection count." 980 | ] 981 | }, 982 | { 983 | "cell_type": "markdown", 984 | "metadata": {}, 985 | "source": [ 986 | "#### Variables for time delta dispersion" 987 | ] 988 | }, 989 | { 990 | "cell_type": "code", 991 | "execution_count": 79, 992 | "metadata": {}, 993 | "outputs": [], 994 | "source": [ 995 | "http_df['tsLow'] = http_df['deltas'].apply(lambda x: np.percentile(np.array(x), 15))\n", 996 | "http_df['tsMid'] = http_df['deltas'].apply(lambda x: np.percentile(np.array(x), 30))\n", 997 | "http_df['tsHigh'] = http_df['deltas'].apply(lambda x: np.percentile(np.array(x), 45))\n", 998 | "http_df['tsMadm'] = http_df['deltas'].apply(lambda x: np.percentile(np.absolute(np.array(x) - np.median(np.array(x))), 30))" 999 | ] 1000 | }, 1001 | { 1002 | "cell_type": "markdown", 1003 | "metadata": {}, 1004 | "source": [ 1005 | "#### Variables for data size dispersion" 1006 | ] 1007 | }, 1008 | { 1009 | "cell_type": "code", 1010 | "execution_count": 80, 1011 | "metadata": {}, 1012 | "outputs": [], 1013 | "source": [ 1014 | "http_df['dsLow'] = http_df[f_sent_bytes].apply(lambda x: np.percentile(np.array(x), 15))\n", 1015 | "http_df['dsMid'] = http_df[f_sent_bytes].apply(lambda x: np.percentile(np.array(x), 30))\n", 1016 | "http_df['dsHigh'] = http_df[f_sent_bytes].apply(lambda x: np.percentile(np.array(x), 45))\n", 1017 | "http_df['dsMadm'] = http_df[f_sent_bytes].apply(lambda x: np.percentile(np.absolute(np.array(x) - np.median(np.array(x))), 30))\n", 1018 | "# Get max of received bytes\n", 1019 | "http_df['dsResponseMax'] = http_df[f_received_bytes].apply(lambda x: max(x))\n", 1020 | "# http_df['dsResponse_last_5'] = http_df[f_received_bytes].apply(lambda x: sorted(x)[-10])" 1021 | ] 1022 | }, 1023 | { 1024 | "cell_type": "code", 1025 | "execution_count": 81, 1026 | "metadata": {}, 1027 | "outputs": [ 1028 | { 1029 | "data": { 1030 | "text/html": [ 1031 | "
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SourceIPDestinationHostNameRequestMethodTimestampDestinationIPDestinationPortSentBytesReceivedBytesconn_countdeltastsLowtsMidtsHightsMadmdsLowdsMiddsHighdsMadmdsResponseMax
010.10.25.52www.amazon.comGET[2023-07-13 11:05:59.388000, 2023-07-13 11:06:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[279, 279, 279, 279, 279, 279, 279, 279, 279, ...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...848[10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,...18.052.056.04.0279.0279.0279.00.0386058
110.10.25.52www.amazon.comPOST[2023-07-13 11:10:33.076000, 2023-07-13 11:10:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[595, 425214, 595, 5116, 595, 992910, 1870023,...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...28[0.0, 0.0, 0.0, 0.0, 4100.0, 123.0, 458.0, 60....47.759.466.113.8595.0595.0944.0420.061478
\n", 1117 | "
" 1118 | ], 1119 | "text/plain": [ 1120 | " SourceIP DestinationHostName RequestMethod \\\n", 1121 | "0 10.10.25.52 www.amazon.com GET \n", 1122 | "1 10.10.25.52 www.amazon.com POST \n", 1123 | "\n", 1124 | " Timestamp \\\n", 1125 | "0 [2023-07-13 11:05:59.388000, 2023-07-13 11:06:... \n", 1126 | "1 [2023-07-13 11:10:33.076000, 2023-07-13 11:10:... \n", 1127 | "\n", 1128 | " DestinationIP \\\n", 1129 | "0 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 1130 | "1 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 1131 | "\n", 1132 | " DestinationPort \\\n", 1133 | "0 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 1134 | "1 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 1135 | "\n", 1136 | " SentBytes \\\n", 1137 | "0 [279, 279, 279, 279, 279, 279, 279, 279, 279, ... \n", 1138 | "1 [595, 425214, 595, 5116, 595, 992910, 1870023,... \n", 1139 | "\n", 1140 | " ReceivedBytes conn_count \\\n", 1141 | "0 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... 848 \n", 1142 | "1 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... 28 \n", 1143 | "\n", 1144 | " deltas tsLow tsMid tsHigh \\\n", 1145 | "0 [10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,... 18.0 52.0 56.0 \n", 1146 | "1 [0.0, 0.0, 0.0, 0.0, 4100.0, 123.0, 458.0, 60.... 47.7 59.4 66.1 \n", 1147 | "\n", 1148 | " tsMadm dsLow dsMid dsHigh dsMadm dsResponseMax \n", 1149 | "0 4.0 279.0 279.0 279.0 0.0 386058 \n", 1150 | "1 13.8 595.0 595.0 944.0 420.0 61478 " 1151 | ] 1152 | }, 1153 | "execution_count": 81, 1154 | "metadata": {}, 1155 | "output_type": "execute_result" 1156 | } 1157 | ], 1158 | "source": [ 1159 | "http_df.head(3)" 1160 | ] 1161 | }, 1162 | { 1163 | "cell_type": "markdown", 1164 | "metadata": {}, 1165 | "source": [ 1166 | "### Interactive Beaconing Duration and Connection Count Validation\n", 1167 | "Calculate duration of the assumed interactive beaconing phase. \n", 1168 | "Based on tsHigh and sum of the deltas up to the tsHigh value in the time delta list, there should be enough connections. \n", 1169 | "A Session with just 5 connections in 10 minutes shouldn't be consdired as beaconing" 1170 | ] 1171 | }, 1172 | { 1173 | "cell_type": "code", 1174 | "execution_count": 82, 1175 | "metadata": {}, 1176 | "outputs": [], 1177 | "source": [ 1178 | "def calc_int_duration(deltas: list) -> float:\n", 1179 | " \"\"\"Calculate the interactivity duration\"\"\"\n", 1180 | " _tmp = np.percentile(np.array(deltas), 45)\n", 1181 | " # if sleep is set to 0, we may see deltas as 0 seconds but the actual time delta is around is 0.2 seconds in that situation.\n", 1182 | " # so, we are adding 0.2 seconds to the delta if it is less than or equal to 0, and getting the sum of all deltas up to the tsHigh which is 45th percentile.\n", 1183 | " return sum([i if i > 0 else 0.2 for i in [num for num in deltas if num <= _tmp]])\n", 1184 | "\n", 1185 | "def calc_int_conn_count(deltas: list) -> int:\n", 1186 | " \"\"\"Calculate the interactivity connection count\"\"\"\n", 1187 | " _tmp = np.percentile(np.array(deltas), 45)\n", 1188 | " return len([i for i in deltas if i <= _tmp])" 1189 | ] 1190 | }, 1191 | { 1192 | "cell_type": "code", 1193 | "execution_count": 83, 1194 | "metadata": {}, 1195 | "outputs": [ 1196 | { 1197 | "data": { 1198 | "text/html": [ 1199 | "
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SourceIPDestinationHostNameRequestMethodTimestampDestinationIPDestinationPortSentBytesReceivedBytesconn_countdeltas...tsMidtsHightsMadmdsLowdsMiddsHighdsMadmdsResponseMaxinteractivity_durationinteractivity_conn_count
010.10.25.52www.amazon.comGET[2023-07-13 11:05:59.388000, 2023-07-13 11:06:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[279, 279, 279, 279, 279, 279, 279, 279, 279, ...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...848[10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,......52.056.04.0279.0279.0279.00.038605813475.2389
110.10.25.52www.amazon.comPOST[2023-07-13 11:10:33.076000, 2023-07-13 11:10:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[595, 425214, 595, 5116, 595, 992910, 1870023,...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...28[0.0, 0.0, 0.0, 0.0, 4100.0, 123.0, 458.0, 60.......59.466.113.8595.0595.0944.0420.061478469.812
\n", 1291 | "

2 rows × 21 columns

\n", 1292 | "
" 1293 | ], 1294 | "text/plain": [ 1295 | " SourceIP DestinationHostName RequestMethod \\\n", 1296 | "0 10.10.25.52 www.amazon.com GET \n", 1297 | "1 10.10.25.52 www.amazon.com POST \n", 1298 | "\n", 1299 | " Timestamp \\\n", 1300 | "0 [2023-07-13 11:05:59.388000, 2023-07-13 11:06:... \n", 1301 | "1 [2023-07-13 11:10:33.076000, 2023-07-13 11:10:... \n", 1302 | "\n", 1303 | " DestinationIP \\\n", 1304 | "0 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 1305 | "1 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 1306 | "\n", 1307 | " DestinationPort \\\n", 1308 | "0 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 1309 | "1 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 1310 | "\n", 1311 | " SentBytes \\\n", 1312 | "0 [279, 279, 279, 279, 279, 279, 279, 279, 279, ... \n", 1313 | "1 [595, 425214, 595, 5116, 595, 992910, 1870023,... \n", 1314 | "\n", 1315 | " ReceivedBytes conn_count \\\n", 1316 | "0 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... 848 \n", 1317 | "1 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... 28 \n", 1318 | "\n", 1319 | " deltas ... tsMid tsHigh \\\n", 1320 | "0 [10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,... ... 52.0 56.0 \n", 1321 | "1 [0.0, 0.0, 0.0, 0.0, 4100.0, 123.0, 458.0, 60.... ... 59.4 66.1 \n", 1322 | "\n", 1323 | " tsMadm dsLow dsMid dsHigh dsMadm dsResponseMax \\\n", 1324 | "0 4.0 279.0 279.0 279.0 0.0 386058 \n", 1325 | "1 13.8 595.0 595.0 944.0 420.0 61478 \n", 1326 | "\n", 1327 | " interactivity_duration interactivity_conn_count \n", 1328 | "0 13475.2 389 \n", 1329 | "1 469.8 12 \n", 1330 | "\n", 1331 | "[2 rows x 21 columns]" 1332 | ] 1333 | }, 1334 | "execution_count": 83, 1335 | "metadata": {}, 1336 | "output_type": "execute_result" 1337 | } 1338 | ], 1339 | "source": [ 1340 | "# interactivity_duration: seconds\n", 1341 | "# min_required_conn_count: minimum number of connections required to be considered as a valid beacon\n", 1342 | "http_df['interactivity_duration'] = http_df['deltas'].apply(lambda x: calc_int_duration(x))\n", 1343 | "http_df['interactivity_conn_count'] = http_df['deltas'].apply(lambda x: calc_int_conn_count(x))\n", 1344 | "http_df.head(2)" 1345 | ] 1346 | }, 1347 | { 1348 | "cell_type": "code", 1349 | "execution_count": 84, 1350 | "metadata": {}, 1351 | "outputs": [ 1352 | { 1353 | "data": { 1354 | "text/html": [ 1355 | "
\n", 1356 | "\n", 1369 | "\n", 1370 | " \n", 1371 | " \n", 1372 | " \n", 1373 | " \n", 1374 | " \n", 1375 | " \n", 1376 | " \n", 1377 | " \n", 1378 | " \n", 1379 | " \n", 1380 | " \n", 1381 | " \n", 1382 | " \n", 1383 | " \n", 1384 | " \n", 1385 | " \n", 1386 | " \n", 1387 | " \n", 1388 | " \n", 1389 | " \n", 1390 | " \n", 1391 | " \n", 1392 | " \n", 1393 | " \n", 1394 | " \n", 1395 | " \n", 1396 | " \n", 1397 | " \n", 1398 | " \n", 1399 | " \n", 1400 | " \n", 1401 | " \n", 1402 | " \n", 1403 | " \n", 1404 | " \n", 1405 | " \n", 1406 | " \n", 1407 | " \n", 1408 | " \n", 1409 | " \n", 1410 | " \n", 1411 | " \n", 1412 | " \n", 1413 | " \n", 1414 | " \n", 1415 | " \n", 1416 | " \n", 1417 | " \n", 1418 | " \n", 1419 | " \n", 1420 | " \n", 1421 | " \n", 1422 | "
SourceIPDestinationHostNameRequestMethodTimestampDestinationIPDestinationPortSentBytesReceivedBytesconn_countdeltas...tsMidtsHightsMadmdsLowdsMiddsHighdsMadmdsResponseMaxinteractivity_durationinteractivity_conn_count
010.10.25.52www.amazon.comGET[2023-07-13 11:05:59.388000, 2023-07-13 11:06:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[279, 279, 279, 279, 279, 279, 279, 279, 279, ...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...848[10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,......52.056.04.0279.0279.0279.00.038605813475.2389
\n", 1423 | "

1 rows × 21 columns

\n", 1424 | "
" 1425 | ], 1426 | "text/plain": [ 1427 | " SourceIP DestinationHostName RequestMethod \\\n", 1428 | "0 10.10.25.52 www.amazon.com GET \n", 1429 | "\n", 1430 | " Timestamp \\\n", 1431 | "0 [2023-07-13 11:05:59.388000, 2023-07-13 11:06:... \n", 1432 | "\n", 1433 | " DestinationIP \\\n", 1434 | "0 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 1435 | "\n", 1436 | " DestinationPort \\\n", 1437 | "0 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 1438 | "\n", 1439 | " SentBytes \\\n", 1440 | "0 [279, 279, 279, 279, 279, 279, 279, 279, 279, ... \n", 1441 | "\n", 1442 | " ReceivedBytes conn_count \\\n", 1443 | "0 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... 848 \n", 1444 | "\n", 1445 | " deltas ... tsMid tsHigh \\\n", 1446 | "0 [10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,... ... 52.0 56.0 \n", 1447 | "\n", 1448 | " tsMadm dsLow dsMid dsHigh dsMadm dsResponseMax \\\n", 1449 | "0 4.0 279.0 279.0 279.0 0.0 386058 \n", 1450 | "\n", 1451 | " interactivity_duration interactivity_conn_count \n", 1452 | "0 13475.2 389 \n", 1453 | "\n", 1454 | "[1 rows x 21 columns]" 1455 | ] 1456 | }, 1457 | "execution_count": 84, 1458 | "metadata": {}, 1459 | "output_type": "execute_result" 1460 | } 1461 | ], 1462 | "source": [ 1463 | "# False Positives can be removed by filtering out the rows with 'min_interactivity_conn_count' less than a threshold.\n", 1464 | "# Additionally, we can filter out the rows with 'interactivity_duration' less than a certain threshold.\n", 1465 | "min_interactivity_duration = 3600 # 1 hour\n", 1466 | "min_interactivity_conn_count = 24 # 24 connections\n", 1467 | "http_df = http_df.loc[(http_df['interactivity_conn_count'] >= min_interactivity_conn_count) & (http_df['interactivity_duration'] >= min_interactivity_duration)]\n", 1468 | "http_df.head()" 1469 | ] 1470 | }, 1471 | { 1472 | "cell_type": "markdown", 1473 | "metadata": {}, 1474 | "source": [ 1475 | "### Execution and Data Size During Interactive Beaconing\n", 1476 | "There should be some connections with higher data size during the interactive beaconing phase that represents the command/tool execution. \n", 1477 | "`count` and `minimum` of the data size of those connections is used as a threshold for FP reduction. For example, if we expect an attacker to perform at least 10 executions involving tools(not just basic commands like whoami), there should be at least 10 connections with data size higher than a threshold. \n", 1478 | "`min_execution_count` and `min_execution_bytes_threshold` are used to reduce the FP. \n" 1479 | ] 1480 | }, 1481 | { 1482 | "cell_type": "code", 1483 | "execution_count": 85, 1484 | "metadata": {}, 1485 | "outputs": [], 1486 | "source": [ 1487 | "min_execution_count = 10\n", 1488 | "min_execution_bytes_threshold = 20000\n", 1489 | "# min_execution_bytes : the last 15th largest value in the list\n", 1490 | "http_df['min_execution_bytes'] = http_df[f_received_bytes].apply(lambda x: sorted(x)[-min_execution_count])\n", 1491 | "\n", 1492 | "# remove the false positives\n", 1493 | "http_df = http_df.query(\"min_execution_bytes > @min_execution_bytes_threshold\")" 1494 | ] 1495 | }, 1496 | { 1497 | "cell_type": "markdown", 1498 | "metadata": {}, 1499 | "source": [ 1500 | "### Calculating the score" 1501 | ] 1502 | }, 1503 | { 1504 | "cell_type": "code", 1505 | "execution_count": 86, 1506 | "metadata": {}, 1507 | "outputs": [], 1508 | "source": [ 1509 | "# Set jitter percentage threshold\n", 1510 | "jitter_treshold = 55 # => 55%\n", 1511 | "\n", 1512 | "# Time delta score calculation\n", 1513 | "def ts_score(tsMadm, tsMid, jitter_threshold = 55):\n", 1514 | " score = 0\n", 1515 | " if tsMid > 0:\n", 1516 | " if (tsMadm / tsMid) * 100 < jitter_threshold:\n", 1517 | " score = 1\n", 1518 | " else:\n", 1519 | " score = 1 - ((tsMadm / tsMid) * 0.4)\n", 1520 | " else:\n", 1521 | " score = 1\n", 1522 | " return score\n", 1523 | "\n", 1524 | "# Data size score calculation\n", 1525 | "def ds_score(dsMadm, dsMid, dsResponseMax, jitter_threshold = 25):\n", 1526 | " score = 0\n", 1527 | " if dsMid > 0:\n", 1528 | " if (dsMadm / dsMid) * 100 < jitter_threshold:\n", 1529 | " score = 1\n", 1530 | " else:\n", 1531 | " score = 1 - ((dsMadm / dsMid) * 0.4)\n", 1532 | " else:\n", 1533 | " score = 1\n", 1534 | " if dsResponseMax < 20000:\n", 1535 | " score = score - 0.3\n", 1536 | " return score\n", 1537 | "\n", 1538 | "http_df['tsScore'] = http_df.apply(lambda x: ts_score(x['tsMadm'], x['tsMid']), axis=1)\n", 1539 | "http_df['dsScore'] = http_df.apply(lambda x: ds_score(x['dsMadm'], x['dsMid'], x['dsResponseMax']), axis=1)\n", 1540 | "\n", 1541 | "# Final Score calculation\n", 1542 | "http_df['Score'] = (http_df['dsScore'] + http_df['tsScore']) / 2\n", 1543 | "\n", 1544 | "# Sort Results\n", 1545 | "http_df.sort_values(by= 'Score', ascending=False, inplace=True, ignore_index=True)\n" 1546 | ] 1547 | }, 1548 | { 1549 | "cell_type": "markdown", 1550 | "metadata": {}, 1551 | "source": [ 1552 | "## Filter Beaconing Traffic with High Score" 1553 | ] 1554 | }, 1555 | { 1556 | "cell_type": "code", 1557 | "execution_count": 87, 1558 | "metadata": {}, 1559 | "outputs": [ 1560 | { 1561 | "data": { 1562 | "text/html": [ 1563 | "
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SourceIPDestinationHostNameRequestMethodTimestampDestinationIPDestinationPortSentBytesReceivedBytesconn_countdeltas...dsMiddsHighdsMadmdsResponseMaxinteractivity_durationinteractivity_conn_countmin_execution_bytestsScoredsScoreScore
010.10.25.52www.amazon.comGET[2023-07-13 11:05:59.388000, 2023-07-13 11:06:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[279, 279, 279, 279, 279, 279, 279, 279, 279, ...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...848[10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,......279.0279.00.038605813475.238979882111.0
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1 rows × 25 columns

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" 1633 | ], 1634 | "text/plain": [ 1635 | " SourceIP DestinationHostName RequestMethod \\\n", 1636 | "0 10.10.25.52 www.amazon.com GET \n", 1637 | "\n", 1638 | " Timestamp \\\n", 1639 | "0 [2023-07-13 11:05:59.388000, 2023-07-13 11:06:... \n", 1640 | "\n", 1641 | " DestinationIP \\\n", 1642 | "0 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 1643 | "\n", 1644 | " DestinationPort \\\n", 1645 | "0 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 1646 | "\n", 1647 | " SentBytes \\\n", 1648 | "0 [279, 279, 279, 279, 279, 279, 279, 279, 279, ... \n", 1649 | "\n", 1650 | " ReceivedBytes conn_count \\\n", 1651 | "0 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... 848 \n", 1652 | "\n", 1653 | " deltas ... dsMid dsHigh \\\n", 1654 | "0 [10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,... ... 279.0 279.0 \n", 1655 | "\n", 1656 | " dsMadm dsResponseMax interactivity_duration interactivity_conn_count \\\n", 1657 | "0 0.0 386058 13475.2 389 \n", 1658 | "\n", 1659 | " min_execution_bytes tsScore dsScore Score \n", 1660 | "0 79882 1 1 1.0 \n", 1661 | "\n", 1662 | "[1 rows x 25 columns]" 1663 | ] 1664 | }, 1665 | "execution_count": 87, 1666 | "metadata": {}, 1667 | "output_type": "execute_result" 1668 | } 1669 | ], 1670 | "source": [ 1671 | "http_df = http_df.query(\"Score > 0.85\")\n", 1672 | "http_df.head()" 1673 | ] 1674 | }, 1675 | { 1676 | "cell_type": "markdown", 1677 | "metadata": {}, 1678 | "source": [ 1679 | "### Enrich Results with Destination Prevalence\n", 1680 | "The prevalence enrichment should be applied after filtering the beaconing traffic with high scores. Otherwise, there is a high chance of false negatives." 1681 | ] 1682 | }, 1683 | { 1684 | "cell_type": "code", 1685 | "execution_count": 88, 1686 | "metadata": {}, 1687 | "outputs": [ 1688 | { 1689 | "data": { 1690 | "text/html": [ 1691 | "
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SourceIPDestinationHostNameRequestMethodTimestampDestinationIPDestinationPortSentBytesReceivedBytesconn_countdeltas...dsHighdsMadmdsResponseMaxinteractivity_durationinteractivity_conn_countmin_execution_bytestsScoredsScoreScoredestination_prevalence
010.10.25.52www.amazon.comGET[2023-07-13 11:05:59.388000, 2023-07-13 11:06:...[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[279, 279, 279, 279, 279, 279, 279, 279, 279, ...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...848[10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,......279.00.038605813475.238979882111.01
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1 rows × 26 columns

\n", 1760 | "
" 1761 | ], 1762 | "text/plain": [ 1763 | " SourceIP DestinationHostName RequestMethod \\\n", 1764 | "0 10.10.25.52 www.amazon.com GET \n", 1765 | "\n", 1766 | " Timestamp \\\n", 1767 | "0 [2023-07-13 11:05:59.388000, 2023-07-13 11:06:... \n", 1768 | "\n", 1769 | " DestinationIP \\\n", 1770 | "0 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... \n", 1771 | "\n", 1772 | " DestinationPort \\\n", 1773 | "0 [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 1774 | "\n", 1775 | " SentBytes \\\n", 1776 | "0 [279, 279, 279, 279, 279, 279, 279, 279, 279, ... \n", 1777 | "\n", 1778 | " ReceivedBytes conn_count \\\n", 1779 | "0 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... 848 \n", 1780 | "\n", 1781 | " deltas ... dsHigh dsMadm \\\n", 1782 | "0 [10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,... ... 279.0 0.0 \n", 1783 | "\n", 1784 | " dsResponseMax interactivity_duration interactivity_conn_count \\\n", 1785 | "0 386058 13475.2 389 \n", 1786 | "\n", 1787 | " min_execution_bytes tsScore dsScore Score destination_prevalence \n", 1788 | "0 79882 1 1 1.0 1 \n", 1789 | "\n", 1790 | "[1 rows x 26 columns]" 1791 | ] 1792 | }, 1793 | "execution_count": 88, 1794 | "metadata": {}, 1795 | "output_type": "execute_result" 1796 | } 1797 | ], 1798 | "source": [ 1799 | "# define columns for prevalnce calculation\n", 1800 | "# Example: count of distinct source IPs for a destination IP AND destination host\n", 1801 | "f_prevalence_src_group = f_src_ip # use only one column (src_ip, src_host, src_user, etc.)\n", 1802 | "f_prevalence_dst_group = f_dst_host\n", 1803 | "dst_prevalence = http_df.groupby(f_prevalence_dst_group)[f_prevalence_src_group].nunique().to_frame().reset_index().rename(columns={f_prevalence_src_group: 'destination_prevalence'})\n", 1804 | "http_df = http_df.merge(dst_prevalence, on=f_prevalence_dst_group, how='left')\n", 1805 | "http_df.head()" 1806 | ] 1807 | }, 1808 | { 1809 | "cell_type": "markdown", 1810 | "metadata": {}, 1811 | "source": [ 1812 | "## Filter Results with Low Prevalence\n" 1813 | ] 1814 | }, 1815 | { 1816 | "cell_type": "code", 1817 | "execution_count": 89, 1818 | "metadata": {}, 1819 | "outputs": [ 1820 | { 1821 | "data": { 1822 | "text/html": [ 1823 | "
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ScoretsScoredsScoreconn_countmin_execution_bytesdsResponseMaxdestination_prevalenceSourceIPDestinationIPDestinationHostNameRequestMethodDestinationPortSentBytesReceivedBytesdeltas
01.01184879882386058110.10.25.52[1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1...www.amazon.comGET[nan, nan, nan, nan, nan, nan, nan, nan, nan, ...[279, 279, 279, 279, 279, 279, 279, 279, 279, ...[257, 257, 257, 257, 257, 257, 257, 257, 257, ...[10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,...
\n", 1879 | "
" 1880 | ], 1881 | "text/plain": [ 1882 | " Score tsScore dsScore conn_count min_execution_bytes dsResponseMax \\\n", 1883 | "0 1.0 1 1 848 79882 386058 \n", 1884 | "\n", 1885 | " destination_prevalence SourceIP \\\n", 1886 | "0 1 10.10.25.52 \n", 1887 | "\n", 1888 | " DestinationIP DestinationHostName \\\n", 1889 | "0 [1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1.2.3.4.5, 1... www.amazon.com \n", 1890 | "\n", 1891 | " RequestMethod DestinationPort \\\n", 1892 | "0 GET [nan, nan, nan, nan, nan, nan, nan, nan, nan, ... \n", 1893 | "\n", 1894 | " SentBytes \\\n", 1895 | "0 [279, 279, 279, 279, 279, 279, 279, 279, 279, ... \n", 1896 | "\n", 1897 | " ReceivedBytes \\\n", 1898 | "0 [257, 257, 257, 257, 257, 257, 257, 257, 257, ... \n", 1899 | "\n", 1900 | " deltas \n", 1901 | "0 [10.0, 155.0, 68.0, 39.0, 0.0, 8.0, 8.0, 54.0,... " 1902 | ] 1903 | }, 1904 | "execution_count": 89, 1905 | "metadata": {}, 1906 | "output_type": "execute_result" 1907 | } 1908 | ], 1909 | "source": [ 1910 | "http_df[columns_to_display].query(\"destination_prevalence < 5\")" 1911 | ] 1912 | } 1913 | ], 1914 | "metadata": { 1915 | "interpreter": { 1916 | "hash": "8b31ef20ae276fecbdd8bf57e61935f42b8506dff183058cb685e575ebb4c990" 1917 | }, 1918 | "kernelspec": { 1919 | "display_name": "Python 3 (ipykernel)", 1920 | "language": "python", 1921 | "name": "python3" 1922 | }, 1923 | "language_info": { 1924 | "codemirror_mode": { 1925 | "name": "ipython", 1926 | "version": 3 1927 | }, 1928 | "file_extension": ".py", 1929 | "mimetype": "text/x-python", 1930 | "name": "python", 1931 | "nbconvert_exporter": "python", 1932 | "pygments_lexer": "ipython3", 1933 | "version": "3.10.9" 1934 | } 1935 | }, 1936 | "nbformat": 4, 1937 | "nbformat_minor": 2 1938 | } 1939 | --------------------------------------------------------------------------------