{"id":23318657,"url":"https://github.com/soulyma/ai-early_detection_of_cyberattacks","last_synced_at":"2026-04-30T14:38:01.481Z","repository":{"id":249770954,"uuid":"832500973","full_name":"Soulyma/AI-Early_Detection_Of_CyberAttacks","owner":"Soulyma","description":"AI-Early_Detection_Of_CyberAttacks_Using_MachineLearning","archived":false,"fork":false,"pushed_at":"2024-07-25T09:27:47.000Z","size":29669,"stargazers_count":3,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-07T05:19:07.595Z","etag":null,"topics":["augmentation","cybersecurity","machine-learning","ml","python","sampling-methods"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","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/Soulyma.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":"2024-07-23T06:43:13.000Z","updated_at":"2025-02-26T19:22:57.000Z","dependencies_parsed_at":"2025-02-13T09:42:35.589Z","dependency_job_id":null,"html_url":"https://github.com/Soulyma/AI-Early_Detection_Of_CyberAttacks","commit_stats":null,"previous_names":["soulyma/ai-early_detection_of_cyberattacks"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/Soulyma/AI-Early_Detection_Of_CyberAttacks","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soulyma%2FAI-Early_Detection_Of_CyberAttacks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soulyma%2FAI-Early_Detection_Of_CyberAttacks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soulyma%2FAI-Early_Detection_Of_CyberAttacks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soulyma%2FAI-Early_Detection_Of_CyberAttacks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Soulyma","download_url":"https://codeload.github.com/Soulyma/AI-Early_Detection_Of_CyberAttacks/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Soulyma%2FAI-Early_Detection_Of_CyberAttacks/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":32468009,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-30T13:12:12.517Z","status":"ssl_error","status_checked_at":"2026-04-30T13:12:06.837Z","response_time":57,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"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":["augmentation","cybersecurity","machine-learning","ml","python","sampling-methods"],"created_at":"2024-12-20T17:17:49.862Z","updated_at":"2026-04-30T14:38:01.453Z","avatar_url":"https://github.com/Soulyma.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AI-Early_Detection_Of_CyberAttacks\nAI-Early_Detection_Of_CyberAttacks_Using_MachineLearning\n\nOur findings provide critical insights into future trends in threat mitigation and incident response strategies.\n\nWithin this repository, you'll find three Jupyter Notebook files (.ipynb). The first focuses on training a\nmodel to predict the presence of a cyberattack. The second one deals with predicting the specific type of cyberattack.\n\nDuring the training process for the cyberattack type prediction model, the initial attempt yielded unsatisfactory results.\nTo address this, we employed the 'SMOTE' library for data augmentation. This involved applying oversampling followed by undersampling to reduce noise within the dataset.\n\n```\n\nfrom imblearn.under_sampling import EditedNearestNeighbours \n\nfrom imblearn.over_sampling import SMOTE\n\nsm = SMOTE(sampling_strategy='all',n_jobs=-1,k_neighbors=3)\n\n\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoulyma%2Fai-early_detection_of_cyberattacks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsoulyma%2Fai-early_detection_of_cyberattacks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoulyma%2Fai-early_detection_of_cyberattacks/lists"}