{"id":20408537,"url":"https://github.com/jcapellman/mlids","last_synced_at":"2025-05-08T14:32:15.858Z","repository":{"id":65528913,"uuid":"69714683","full_name":"jcapellman/MLIDS","owner":"jcapellman","description":"Machine Learning Intrusion Detection and Network Monitor ","archived":false,"fork":false,"pushed_at":"2024-08-24T23:24:02.000Z","size":1531,"stargazers_count":13,"open_issues_count":7,"forks_count":4,"subscribers_count":6,"default_branch":"master","last_synced_at":"2024-08-25T00:27:59.569Z","etag":null,"topics":["csharp","dotnet-core","ids","machine-learning","ml-net"],"latest_commit_sha":null,"homepage":"","language":"C#","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jcapellman.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":"SECURITY.md","support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2016-10-01T02:12:54.000Z","updated_at":"2024-08-24T23:24:06.000Z","dependencies_parsed_at":"2023-02-15T09:16:49.627Z","dependency_job_id":"a76b5b46-b2b8-456e-9355-741528f51bee","html_url":"https://github.com/jcapellman/MLIDS","commit_stats":null,"previous_names":["jcapellman/jcids"],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcapellman%2FMLIDS","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcapellman%2FMLIDS/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcapellman%2FMLIDS/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jcapellman%2FMLIDS/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jcapellman","download_url":"https://codeload.github.com/jcapellman/MLIDS/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":224737397,"owners_count":17361345,"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":["csharp","dotnet-core","ids","machine-learning","ml-net"],"created_at":"2024-11-15T05:33:30.534Z","updated_at":"2024-11-15T05:33:31.470Z","avatar_url":"https://github.com/jcapellman.png","language":"C#","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MLIDS\n\nMLIDS is a Host Intrusion Detection System using Machine Learning.  Several years ago (2014), the original idea was to write a C++ brute force network analyzer for a Cobalt Qube (http://www.jarredcapellman.com/2014/3/9/NetBSD-and-a-Cobalt-Qube-2).  Fast-forward a few years, and my shift to utilizing Machine Learning (ML) every day professionally was a perfect fit for using ML. When it came time to decide on a topic for my dissertation research, this was at the top of my list.\n\nOver the course of working on my dissertation, holding positions within my previous company, and starting a new job, I decided to expand the scope of the work. I primarily utilized Python and Rust for the training and inference, respectively, as opposed to using C# for everything. Going back to one of my core principles - using the best tool for the job. This of course affected my Chapter 3, but would make the research much more real-world.\n\n\n## Status of GitHub Actions\n[![SonarQube Analysis](https://github.com/jcapellman/MLIDS/actions/workflows/SonarQubeAnalysis.yml/badge.svg)](https://github.com/jcapellman/MLIDS/actions/workflows/SonarQubeAnalysis.yml)\n\n[![CodeQL](https://github.com/jcapellman/MLIDS/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/jcapellman/MLIDS/actions/workflows/codeql-analysis.yml)\n\n## Components\nAs anyone who has followed my work over the last two decades - I like to use the right tools for the job. O\n* Packet Capture Driver (NPCAP NDIS Filter Driver - https://nmap.org/npcap/)\n* Packet Capture Application (.NET 8)\n* Model Trainer Application (Python)\n* Model Harness Application (Rust)\n  \n## Releases\nAll artifacts will be built and packaged individually using GitHub Actions. In addition, SonarQube Analysis is being performed for Unit Test coverage, vulnerabilities, bugs, and enterprise readiness.\n\n## Requirements\n* Windows 10+\n* Npcap Driver Installed\n* MongoDB Installed or use of LiteDB, JSON or CSV if storing of the data is needed\n* .NET 8 Runtime (https://dotnet.microsoft.com/en-us/download/dotnet/8.0)\n* Python 3.12.5 (https://www.python.org/)\n* Rust 1.80.1 (https://www.rust-lang.org/)\n\n## Usage\nThe idea is to follow the steps:\n1. Run the Packet Capture Application to generate a sizeable training and test set\n2. Run the Model Trainer Application to generate a model\n3. Run the Model within the Model Harness Application to verify performance impact and detection capabilities\n\n## License\nAs noted this is licensed under the GPL-3.0 License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcapellman%2Fmlids","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjcapellman%2Fmlids","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjcapellman%2Fmlids/lists"}