{"id":23272452,"url":"https://github.com/gaurav0502/malware-classification","last_synced_at":"2025-10-27T13:30:58.978Z","repository":{"id":165648020,"uuid":"577665403","full_name":"Gaurav0502/malware-classification","owner":"Gaurav0502","description":"Malware Byteplot Image Classification using Machine Learning and Deep Learning","archived":false,"fork":false,"pushed_at":"2023-10-09T09:20:24.000Z","size":917587,"stargazers_count":13,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-12-19T19:17:28.127Z","etag":null,"topics":["byteplot-image-classification","computer-vision","convolutional-neural-networks","deep-learning","image-classification","malimg-dataset","malware-analysis","malware-classification","python","tensorflow"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/2310.02742","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/Gaurav0502.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":"2022-12-13T08:50:03.000Z","updated_at":"2024-12-09T06:07:58.000Z","dependencies_parsed_at":null,"dependency_job_id":"1da9e287-72ff-469a-92aa-14258ce98711","html_url":"https://github.com/Gaurav0502/malware-classification","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/Gaurav0502%2Fmalware-classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gaurav0502%2Fmalware-classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gaurav0502%2Fmalware-classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Gaurav0502%2Fmalware-classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Gaurav0502","download_url":"https://codeload.github.com/Gaurav0502/malware-classification/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":238497545,"owners_count":19482272,"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":["byteplot-image-classification","computer-vision","convolutional-neural-networks","deep-learning","image-classification","malimg-dataset","malware-analysis","malware-classification","python","tensorflow"],"created_at":"2024-12-19T19:17:33.828Z","updated_at":"2025-10-27T13:30:57.913Z","avatar_url":"https://github.com/Gaurav0502.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Malware Byteplot Image Classification\n\n## Aim\n\nA comparison of variation in model convergence and performance with change in class imbalance. To assess the variation three datasets are created with varying imbalance in class distribution namely Malimg dataset, Malevis dataset, and Blended dataset. For comparison, the state-of-the-art CNNs from Keras library are explored namely: InceptionNet, ResNet50, DenseNet169, EfficientNetB4, InceptionResNetV2, and VGG16 \n\n## Datasets\n\n- The Malimg dataset is available \u003ca href=\"https://www.kaggle.com/datasets/manaswinisunkari/malimg-dataset9010\"\u003ehere\u003c/a\u003e.\n- The Malevis dataset is available \u003ca href=\"https://www.kaggle.com/datasets/sohamkumar1703/malevis-dataset\"\u003ehere\u003c/a\u003e and its website is \u003ca href=\"https://web.cs.hacettepe.edu.tr/~selman/malevis/\"\u003ehere\u003c/a\u003e.\n- The Blended Malware dataset is available \u003ca href=\"https://www.kaggle.com/datasets/gauravpendharkar/blended-malware-image-dataset/settings\"\u003ehere\u003c/a\u003e.\n\n## Kaggle Notebooks\nThe tasks of Malware Classification and a minor fix to it were carried out in Kaggle using GPU P100. The codes can be found in the respective notebooks:\n- \u003ca href=\"https://www.kaggle.com/code/gauravpendharkar/malware-classification\"\u003eMalware Classification\u003c/a\u003e.\n- \u003ca href=\"https://www.kaggle.com/code/gauravpendharkar/malware-classification-fixed\"\u003eMalware Classification (Fixed)\u003c/a\u003e.\n\n## Results\nThe tabulated results for the comparison are shown in the figure below:\n\n![](https://github.com/Gaurav0502/malware-classification/blob/main/results/tabulated-results.png)\n\n## Conference Paper Citation\n```\n@misc{m2023comparative,\n      title={Comparative Analysis of Imbalanced Malware Byteplot Image Classification using Transfer Learning}, \n      author={Jayasudha M and Ayesha Shaik and Gaurav Pendharkar and Soham Kumar and Muhesh Kumar B and Sudharshanan Balaji},\n      year={2023},\n      eprint={2310.02742},\n      archivePrefix={arXiv},\n      primaryClass={cs.LG}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgaurav0502%2Fmalware-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fgaurav0502%2Fmalware-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fgaurav0502%2Fmalware-classification/lists"}