This project focuses on detecting malicious software (malware) using advanced machine learning techniques. It analyzes malware based on both static and dynamic features, enhancing detection accuracy and scalability. The goal is to detect malware that might evade signature-based or static analysis methods.
We use two main approaches to detect malware:
- Static Analysis 🧑💻: Identifies malware using internal code features from binary or executable files.
- Dynamic Analysis ⚙️: Detects malware by analyzing its behavior in a controlled environment, helping identify advanced threats that static methods might miss.
Three machine learning algorithms were applied to improve detection accuracy and minimize false positives/negatives:
- K-Nearest Neighbors (KNN) 📊
- Random Forest 🌳
- Logistic Regression 📝
These models extract and analyze key features from malware samples to distinguish between benign and malicious files.
- False Positives & Negatives
⚠️ : ML models can sometimes flag benign files as malware (false positives) or miss malicious files (false negatives), which require manual review to avoid missing critical threats. - Feature Selection 🔑: Selecting relevant features from malware samples is critical for accuracy and efficiency, as irrelevant features may reduce model performance.
- Numpy: Perform a variety of mathematical operations on arrays.
- Pandas: Data manipulation and analysis.
- Pickle: Serialize data into binary format and deserialize it back into Python objects.
- Matplotlib: Creating visualizations.
- Sklearn: An open-source data analysis library used for machine learning models.
The project includes a user-friendly interface that allows users to upload a file. The system analyzes the file and classifies it as either benign or malware based on the extracted features and behavior analysis.
To run the project locally, clone the repository and install the required dependencies:
git clone https://github.com/yourusername/malware-detection.git
cd malware-detection
pip install -r requirements.txt