{"id":22589155,"url":"https://github.com/vidhi1290/malware-detection","last_synced_at":"2026-04-27T21:31:51.625Z","repository":{"id":195176792,"uuid":"692412058","full_name":"Vidhi1290/Malware-Detection","owner":"Vidhi1290","description":"Welcome to the Malicious Executable Detection project! 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This repository explores the world of machine learning and clustering analysis to detect malicious executable files. 🔍🤖\n\n## Problem Statement 🎯\nIn an era where cyber warfare is on the rise, detecting malicious code has become crucial. This project aims to develop a machine learning approach to identify malicious executable files. 💻🦠\n\n## Understanding the Data and Attributes 📚\nThe dataset contains features extracted from both malicious and non-malicious Windows executable files. It includes a total of 373 samples, with 301 being malicious and 72 non-malicious files. The dataset is imbalanced, with 531 features represented as F1, F2, and so on, and a label column indicating whether the file is malicious or non-malicious. 📈🧐\n\n## Data Preparation 🛠️\n- **Imputation**: Rows and columns with missing data exceeding 70% are removed. 🧹\n- **Feature Selection**: Relevant features are chosen for analysis. 🎯\n- **Data Standardization**: Standardization is applied to make the data suitable for clustering. 📊\n\n## K-Means Clustering 📈\nK-Means clustering is applied to group similar instances together. The Silhouette method is used to determine the optimal number of clusters. 🧩\n\n## Silhouette Analysis 📊\nSilhouette analysis helps evaluate the quality of clustering. A higher silhouette score indicates better clustering. 📈🔍\n\n## Cluster Stability Check 🔒\nCluster stability is assessed by comparing clusters with and without random sampling of data. 🔄\n\n## Categorizing New Samples 🆕\nThe model is used to predict clusters for new executable files. 📋\n\n## Learning Outcomes 📚\n- Implementing cluster analysis in Python\n- Pre-processing data for analysis\n- Hierarchical clustering and dendrogram visualization\n- Implementing K-Means clustering\n- Determining the optimal number of clusters\n- Cluster stability evaluation\n- Predicting clusters for new samples\n\nFeel free to explore the notebooks and the code to dive deeper into the analysis!\n\n## Kaggle Notebook 📊\nYou can also view this project on [Kaggle](#Kaggle). 📑\n\n## Open in Colab 🚀\nWant to run the notebooks in Google Colab? Click [here](#Open-In-Colab) to open them directly! 💡\n\n## Connect with Us 🌐\nJoin our community and stay updated on our latest projects:\n\n- 🌐 [GitHub](https://github.com/Vidhi1290)\n- 🔗 [LinkedIn](https://www.linkedin.com/in/vidhi-waghela-434663198/)\n- 🐦 [Twitter](https://twitter.com/VidhiWaghela)\n- 📝 [Medium](https://medium.com/@datasciencemeetscybersecurity)\n\nHappy coding! 👩‍💻👨‍💻\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvidhi1290%2Fmalware-detection","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvidhi1290%2Fmalware-detection","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvidhi1290%2Fmalware-detection/lists"}