{"id":19691831,"url":"https://github.com/yueyuel/xaiforandroidmalware","last_synced_at":"2025-04-29T09:31:19.161Z","repository":{"id":114290258,"uuid":"527156249","full_name":"yueyueL/XAIforAndroidMalware","owner":"yueyueL","description":"Explainable AI for Android Malware Detection: Towards Understanding Why the Models Perform So Well?","archived":false,"fork":false,"pushed_at":"2022-08-24T05:42:48.000Z","size":21837,"stargazers_count":13,"open_issues_count":0,"forks_count":2,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-05T14:41:54.609Z","etag":null,"topics":["android-app","explainable-ai","malware-detection","reliability"],"latest_commit_sha":null,"homepage":"","language":null,"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/yueyueL.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}},"created_at":"2022-08-21T09:10:40.000Z","updated_at":"2024-11-11T20:55:57.000Z","dependencies_parsed_at":"2023-09-25T06:57:53.369Z","dependency_job_id":null,"html_url":"https://github.com/yueyueL/XAIforAndroidMalware","commit_stats":{"total_commits":4,"total_committers":2,"mean_commits":2.0,"dds":0.25,"last_synced_commit":"715e1335000d775f4bcb53ca20fc57c7f073910f"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yueyueL%2FXAIforAndroidMalware","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yueyueL%2FXAIforAndroidMalware/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yueyueL%2FXAIforAndroidMalware/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yueyueL%2FXAIforAndroidMalware/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/yueyueL","download_url":"https://codeload.github.com/yueyueL/XAIforAndroidMalware/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":251473234,"owners_count":21595026,"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":["android-app","explainable-ai","malware-detection","reliability"],"created_at":"2024-11-11T19:11:17.323Z","updated_at":"2025-04-29T09:31:14.146Z","avatar_url":"https://github.com/yueyueL.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"# XAI for Android Malware Detection\nThis repository contains the replication package and dataset of the paper published at ISSRE 2022 with the title [**Explainable AI for Android Malware Detection: Towards Understanding Why the Models Perform So Well?**](https://www.researchgate.net/publication/362885641_Explainable_AI_for_Android_Malware_Detection_Towards_Understanding_Why_the_Models_Perform_So_Well)\n\nFor more information, interested researchers can contact us by sending an email to yuehhhliu@gmail.edu. The full dataset is available below.\n\n## Reproduced package\nIn this work, we replicate three high-profile ML-based Android malware detection approaches. \n* Drebin: [pdf](https://prosec.mlsec.org/docs/2014-ndss.pdf), [reproduction code](https://github.com/MLDroid/drebin)\n* XMal: [pdf](https://dl.acm.org/doi/10.1145/3423096), [reproduction code](https://github.com/wubozhi/Xmal)\n* Fan et al.: [pdf](https://ieeexplore.ieee.org/abstract/document/9186721), [reproduction code for models](https://scikit-learn.org/stable/supervised_learning.html), [reproduction code for LIME](https://github.com/marcotcr/lime)\n\n## Data\nThe data folder contains the metadata of Android apps from [AndroZoo](https://androzoo.uni.lu/). AndroZoo is a online collection of Android Applications collected from several sources, including the official Google Play app market. To how to download the dataset, please visit [AndroZoo API Documentation](https://androzoo.uni.lu/api_doc). \n\nThe Android samples span across a 10-year period from 2011 to 2020. The dataset is divided into two parts: benign and malicious samples. We put the metadata (e.g., sha256, md5, market, package name, size) of the samples from different periods into different folders. \n\nFor the ground-truth of temporal information of features (i.e., permission and API calls), the researchers can refer the recent Android Developer Documentation [link](https://developer.android.com/). We put the ground-truth of Android SDK 30 (i.e., data/api-versions.xml) into the data folder.\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyueyuel%2Fxaiforandroidmalware","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyueyuel%2Fxaiforandroidmalware","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyueyuel%2Fxaiforandroidmalware/lists"}