{"id":24405115,"url":"https://github.com/farzeennimran/fake-website-detection","last_synced_at":"2026-04-15T13:31:48.903Z","repository":{"id":272893604,"uuid":"918092724","full_name":"farzeennimran/Fake-website-detection","owner":"farzeennimran","description":"AI to detect malacious websites using ML and DL models","archived":false,"fork":false,"pushed_at":"2025-01-25T23:09:16.000Z","size":1730,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-14T02:11:57.365Z","etag":null,"topics":["artificial-intelligence","data-science","deep-learning","detection-model","fake-website","machine-learning","malcious-files","python3"],"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/farzeennimran.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":"2025-01-17T08:33:16.000Z","updated_at":"2025-01-25T23:09:20.000Z","dependencies_parsed_at":"2025-01-18T00:46:41.147Z","dependency_job_id":null,"html_url":"https://github.com/farzeennimran/Fake-website-detection","commit_stats":null,"previous_names":["farzeennimran/fake-website-detection"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/farzeennimran%2FFake-website-detection","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/farzeennimran%2FFake-website-detection/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/farzeennimran%2FFake-website-detection/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/farzeennimran%2FFake-website-detection/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/farzeennimran","download_url":"https://codeload.github.com/farzeennimran/Fake-website-detection/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243510127,"owners_count":20302294,"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":["artificial-intelligence","data-science","deep-learning","detection-model","fake-website","machine-learning","malcious-files","python3"],"created_at":"2025-01-20T04:16:29.987Z","updated_at":"2025-11-03T01:05:27.358Z","avatar_url":"https://github.com/farzeennimran.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Fake-website-detection\n\nGrowing phishing attacks represent a serious challenge to the security of the computing\nenvironment-the large number of spurious websites motivated by deception and to extract\nsensitive information from individuals. This project proposes to reduce by exploring the transition from\nmachine learning approaches to deep learning approaches for fake website detection.\n\nDeep learning models, by their very nature, can automatically extract features and,\ntherefore, are capable of identifying complex patterns and nuanced relationships within the\ndata, which would otherwise be missed by traditional techniques. Based on methodologies\nlike CNNs and RNNs etc, the proposed system efficiently captures complicated, deep features\nfrom the URLs and web content, therefore augmenting its ability to detect sophisticated\nphishing techniques. Enhancement of both the precision in detection and model adaptability\nto the evolving phishing tactics are considered as priorities. A comprehensive assessment\nshows that deep learning significantly outperforms traditional approaches, thereby\nfacilitating a more meaningful sense of improvement in security measures in the fight against\nphishing attacks\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarzeennimran%2Ffake-website-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffarzeennimran%2Ffake-website-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffarzeennimran%2Ffake-website-detection/lists"}