{"id":13845292,"url":"https://github.com/CyberDruid-Codes/Automated-Reconator","last_synced_at":"2025-07-12T01:32:30.207Z","repository":{"id":223640348,"uuid":"424342030","full_name":"CyberDruid-Codes/Automated-Reconator","owner":"CyberDruid-Codes","description":null,"archived":false,"fork":false,"pushed_at":"2022-06-05T14:38:28.000Z","size":260,"stargazers_count":10,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-11-21T18:39:21.065Z","etag":null,"topics":["cve-scanning","deep-learning","ethical-hacking","ethical-hacking-tools","interpreter","mitre-attack","nmap-scripts","penetration-testing-tools","python","reconnaissance","reporting","testing-tools","vulnerability-scanners"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/CyberDruid-Codes.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2021-11-03T18:43:19.000Z","updated_at":"2024-07-14T16:55:13.000Z","dependencies_parsed_at":null,"dependency_job_id":"b3b20a1f-339f-4cae-a4b1-d6e011dea6f3","html_url":"https://github.com/CyberDruid-Codes/Automated-Reconator","commit_stats":null,"previous_names":["cyberdruid-codes/automated-reconator"],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/CyberDruid-Codes/Automated-Reconator","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CyberDruid-Codes%2FAutomated-Reconator","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CyberDruid-Codes%2FAutomated-Reconator/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CyberDruid-Codes%2FAutomated-Reconator/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CyberDruid-Codes%2FAutomated-Reconator/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/CyberDruid-Codes","download_url":"https://codeload.github.com/CyberDruid-Codes/Automated-Reconator/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/CyberDruid-Codes%2FAutomated-Reconator/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264923076,"owners_count":23683717,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cve-scanning","deep-learning","ethical-hacking","ethical-hacking-tools","interpreter","mitre-attack","nmap-scripts","penetration-testing-tools","python","reconnaissance","reporting","testing-tools","vulnerability-scanners"],"created_at":"2024-08-04T17:03:19.140Z","updated_at":"2025-07-12T01:32:30.162Z","avatar_url":"https://github.com/CyberDruid-Codes.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"```html\n       d8888          888                                   888                 888      8888888b.                                             888                    \n      d88888          888                                   888                 888      888   Y88b                                            888                    \n     d88P888          888                                   888                 888      888    888                                            888                    \n    d88P 888 888  888 888888 .d88b.  88888b.d88b.   8888b.  888888 .d88b.   .d88888      888   d88P .d88b.   .d8888b .d88b.  88888b.   8888b.  888888 .d88b.  888d888 \n   d88P  888 888  888 888   d88\"\"88b 888 \"888 \"88b     \"88b 888   d8P  Y8b d88\" 888      8888888P\" d8P  Y8b d88P\"   d88\"\"88b 888 \"88b     \"88b 888   d88\"\"88b 888P\"   \n  d88P   888 888  888 888   888  888 888  888  888 .d888888 888   88888888 888  888      888 T88b  88888888 888     888  888 888  888 .d888888 888   888  888 888     \n d8888888888 Y88b 888 Y88b. Y88..88P 888  888  888 888  888 Y88b. Y8b.     Y88b 888      888  T88b Y8b.     Y88b.   Y88..88P 888  888 888  888 Y88b. Y88..88P 888     \nd88P     888  \"Y88888  \"Y888 \"Y88P\"  888  888  888 \"Y888888  \"Y888 \"Y8888   \"Y88888      888   T88b \"Y8888   \"Y8888P \"Y88P\"  888  888 \"Y888888  \"Y888 \"Y88P\"  888     \n```                                                                                                                                                                     \n                  \n\n           \n           \n#### ----Description---- ####\n\nThe Automated Agent-Based Model for Penetration Testing is a tool that tackles one main problem, it cuts the complexity of the First Step in the Penetration Testing Process \n(Reconnaissance) and interpretation of the results, by utilizing Deep Neural Networks.\n\n#### ----Why use the Automated Reconator?---- ####\n\nThe tool's Aim is to Scan the Network (Recon) and check for possible vulnerabilities / CVE’s, interpret the scan results using a Deep Learning model and generate a detailed Report which is visible, easy to read and understand.        \nThis will help the cybersecurity experts to spend less on this first step (Reconnaissance), without missing any information, the report being easy to present, as by gathering the results and automatically generating a detailed PDF file. \n\n#### ----Python modules used---- ####\n\nThe Following Python modules(as the guidelines or foundations for the components): Tkinter for the GUI, Nmap for the network scan, VulnSearch for the vulnerability search, ReportLab for \nthe automated PDF Generation, tf.keras for the deep neural network model, Pyattck for the MITRE ATT\u0026CK Framework and Networkx for the techniques/sub-techniques Graph Generator.\n\n#### ----Prerequisites---- ####\n                                                                                                                                                                      \nThe script requires Python 3.8 installed, as a minimum requirement.          \nFor the script to run fully, Python 3.7 needs to be installed too. This is for the optional deep learning module to run, as it utilizes tf.keras.\n\nThe optional vulnerability check module requires an api key added on line 11 of the \"vulnclass.py\" file. \nThe API key can be requested from here, for free! -\u003e https://vulners.com/ (General API Key) \n    \nTo get the required python modules, run the following:           \n- python3.8 python3.8_setup.py              \n- python3.7 python3.7_setup.py            \n              \n(2 scripts for the 2 different python modules. Pip needs to be installed on python before running the script!) \n\n#### ----Prerequisites / Extra Setup ---- ####\nThe following modules need to be edited: cyberbotcall.py and vulncls.py. \n\nThe cyberbotcall.py has to have the path of the two scripts (i.e. cyberbot and learning.py). You can easily add that once you have the scripts locally.\nThe Vulncls has to have your API key at the top of the script. \n\nHere is an explanation on how to have 2 python versions running on the same machine(For the deep learning module): \nhttps://towardsdatascience.com/installing-multiple-alternative-versions-of-python-on-ubuntu-20-04-237be5177474\n\n#### ----How to use---- ####\n\nStep 1: Download/clone the scripts locally                             \nStep 2: Check the Prerequisites                     \nStep 3: Run the Pentestertool.py (double click or through any CLI)                           \nStep 4: Select the appropriate values for the 2 optional components (Have they been configured? No -\u003e Press False)            \nStep 5: Input the IP and Press Submit (Make sure the host is reachable from your machine)             \nStep 6: Wait for the scripts to run              \nStep 7: Press on the \"Generate Report\" button (This and the individual files will be generated at the location of the script)                  \n       \nNote - Make sure the script has the permission to run on your machine and that the host is reachable from your machine     \n\n\n#### ----Demo---- ####\nYou can watch my youtube video to see the script in action!        \nLink:          \nhttps://www.youtube.com/watch?v=GYNTDR-vtng\u0026ab_channel=CyberDruid\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FCyberDruid-Codes%2FAutomated-Reconator","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FCyberDruid-Codes%2FAutomated-Reconator","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FCyberDruid-Codes%2FAutomated-Reconator/lists"}