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license: mit
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# SMU_MalwareDetection PE Assembly Dataset

This dataset consists of assembly code fragments extracted from malicious and benign Portable Executable (PE) files. It was created to support deep learning-based malware classification. Assembly code was segmented using a sliding window technique to preserve contextual information.

## ⚠️ Note: Class Imbalance

This dataset has a **class imbalance**, with fewer benign samples (`0` label) compared to malware samples (`1` label).  
While the difference is not extreme, users should be aware of this imbalance and consider applying appropriate techniques (e.g., class weighting, sampling strategies) during training or evaluation.

## Dataset Description

The dataset includes assembly code segments derived from two types of PE files:

- **Malware Samples**  
  Malicious PE files were downloaded from [MalwareBazaar](https://bazaar.abuse.ch/), specifically focusing on samples uploaded in **2020**. Each file was disassembled into assembly code using the `objdump` command on Ubuntu.

- **Benign Samples**  
  Benign PE files were collected from regular executable files stored on a PC using PowerShell. These files were also disassembled using `objdump` to extract assembly code in the same manner.

## Sliding Window Preprocessing

To prepare the data for machine learning:

- Assembly code was segmented using a sliding window approach:
  - **Window Size:** 350 lines
  - **Stride:** 175 lines (50% overlap)
- This setup ensures that the segments preserve contextual relationships between instructions.
- Importantly, **all segments generated from a single file are kept together** to maintain file-level grouping and avoid mixing across files.
- No tokenization was applied; the raw assembly code was used as-is during segmentation.

## Data Format

Each entry in the dataset includes:

- `assembly`: A segment of assembly code extracted via sliding window
- `label`: A binary classification label indicating `malware` or `benign`

## Intended Use

This dataset is suitable for the following tasks:

- Malware classification using NLP-based models (e.g., Transformer-based models)
- Research on malware detection using sliding window context segmentation

## Tools Used

- `objdump` (for extracting assembly code)
- PowerShell (for collecting benign files)
- Python scripts (for sliding window segmentation and maintaining per-file chunk structure)