{"id":19450986,"url":"https://github.com/alex-snd/malwareclassifier","last_synced_at":"2025-04-25T04:30:23.096Z","repository":{"id":114411558,"uuid":"349950409","full_name":"alex-snd/MalwareClassifier","owner":"alex-snd","description":"👾 Malware Classification using Deep Learning and Cuckoo Sandbox","archived":false,"fork":false,"pushed_at":"2022-06-18T08:21:28.000Z","size":10555,"stargazers_count":14,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-04-03T15:44:11.806Z","etag":null,"topics":["cuckoo-sandbox","cvae","data-science","deep-learning","malware","malware-classification","malware-detection","python","pytorch","vae"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/alex-snd.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","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":"2021-03-21T09:19:42.000Z","updated_at":"2024-10-01T08:17:08.000Z","dependencies_parsed_at":null,"dependency_job_id":"c06d475c-5eee-4c06-9c5b-8c4403c576f4","html_url":"https://github.com/alex-snd/MalwareClassifier","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alex-snd%2FMalwareClassifier","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alex-snd%2FMalwareClassifier/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alex-snd%2FMalwareClassifier/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/alex-snd%2FMalwareClassifier/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/alex-snd","download_url":"https://codeload.github.com/alex-snd/MalwareClassifier/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":250754495,"owners_count":21481822,"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":["cuckoo-sandbox","cvae","data-science","deep-learning","malware","malware-classification","malware-detection","python","pytorch","vae"],"created_at":"2024-11-10T16:39:49.195Z","updated_at":"2025-04-25T04:30:23.079Z","avatar_url":"https://github.com/alex-snd.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Malware Classifier\n\nThis is the code repository for **Malware Classification Research**. All the deep learning models are implemented with Python 3.6+ and PyTorch 1.9.\n\n## Data\nThe source data is the json reports generated by malicious software dynamic analysis system [Cuckoo Sandbox](https://cuckoosandbox.org/).\nThe data was analyzed in order to extract the most useful information about malicious samples. As a result of the analysis, 3698 features were selected, on the basis of which further classification will be carried out. Thus, each instance of malware is assigned a binary feature vector of dimension 3698, the label of which is the result of classification by Kaspersky anti-virus. The database contains about 10,000 labeled samples from 8 different types of malware and about 14,000 unlabeled samples.\n\n## Data Visualization\nThe normalized vector of dimension 3698 is represented as an RGB image of the size 61 × 61 (61 ≈ √3698), in which the color of each pixel is set by the value of the corresponding feature.\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"images/visual_representation.png\" /\u003e\n\u003c/p\u003e\n\n## Autoencoder\nAn autoencoder model with a latent space dimension of 200 was trained on the unlabeled data for further malware classification using pretrained encoder.\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"images/ae_performance.png\" /\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n  \u003ci\u003eAE performance, the first row is input, the second is AE output\u003c/i\u003e\n\u003c/p\u003e\n\nAlso the autoencoder was trained with the size of the latent space equal to 2 for its subsequent visualization on a two-dimensional plane.\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"images/latent_space.gif\"/\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n  \u003ci\u003eChanging the latent space in the learning process\u003c/i\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"images/labeled_scatter.png\"/\u003e\n\u003c/p\u003e\n\u003cp align=\"center\"\u003e\n  \u003ci\u003eLabeled malware samples displayed in latent space\u003c/i\u003e\n\u003c/p\u003e\n\n## Classifier\nСlassifier results:\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"images/classifier_results.png\"/\u003e\n\u003c/p\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falex-snd%2Fmalwareclassifier","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Falex-snd%2Fmalwareclassifier","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Falex-snd%2Fmalwareclassifier/lists"}