https://github.com/vidhi1290/malware-detection
Welcome to the Malicious Executable Detection project! This repository explores the world of machine learning and clustering analysis to detect malicious executable files 🔥🔐
https://github.com/vidhi1290/malware-detection
clustering-algorithm cybersecurity hierarchical-clustering k-means-clustering machine-learning malware-detection python silhouette
Last synced: about 2 months ago
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Welcome to the Malicious Executable Detection project! This repository explores the world of machine learning and clustering analysis to detect malicious executable files 🔥🔐
- Host: GitHub
- URL: https://github.com/vidhi1290/malware-detection
- Owner: Vidhi1290
- Created: 2023-09-16T11:53:38.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-16T12:01:34.000Z (over 1 year ago)
- Last Synced: 2025-02-02T18:33:26.205Z (4 months ago)
- Topics: clustering-algorithm, cybersecurity, hierarchical-clustering, k-means-clustering, machine-learning, malware-detection, python, silhouette
- Language: Jupyter Notebook
- Homepage:
- Size: 12.7 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Malicious Executable Detection using Cluster Analysis 📊
Welcome to the Malicious Executable Detection project! This repository explores the world of machine learning and clustering analysis to detect malicious executable files. 🔍🤖
## Problem Statement 🎯
In 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. 💻🦠
## Understanding the Data and Attributes 📚
The 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. 📈🧐
## Data Preparation 🛠️
- **Imputation**: Rows and columns with missing data exceeding 70% are removed. 🧹
- **Feature Selection**: Relevant features are chosen for analysis. 🎯
- **Data Standardization**: Standardization is applied to make the data suitable for clustering. 📊
## K-Means Clustering 📈
K-Means clustering is applied to group similar instances together. The Silhouette method is used to determine the optimal number of clusters. 🧩
## Silhouette Analysis 📊
Silhouette analysis helps evaluate the quality of clustering. A higher silhouette score indicates better clustering. 📈🔍
## Cluster Stability Check 🔒
Cluster stability is assessed by comparing clusters with and without random sampling of data. 🔄
## Categorizing New Samples 🆕
The model is used to predict clusters for new executable files. 📋
## Learning Outcomes 📚
- Implementing cluster analysis in Python
- Pre-processing data for analysis
- Hierarchical clustering and dendrogram visualization
- Implementing K-Means clustering
- Determining the optimal number of clusters
- Cluster stability evaluation
- Predicting clusters for new samples
Feel free to explore the notebooks and the code to dive deeper into the analysis!
## Kaggle Notebook 📊
You can also view this project on [Kaggle](#Kaggle). 📑
## Open in Colab 🚀
Want to run the notebooks in Google Colab? Click [here](#Open-In-Colab) to open them directly! 💡
## Connect with Us 🌐
Join our community and stay updated on our latest projects:
- 🌐 [GitHub](https://github.com/Vidhi1290)
- 🔗 [LinkedIn](https://www.linkedin.com/in/vidhi-waghela-434663198/)
- 🐦 [Twitter](https://twitter.com/VidhiWaghela)
- 📝 [Medium](https://medium.com/@datasciencemeetscybersecurity)
Happy coding! 👩💻👨💻