README.md

metadata

license: mit

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, 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)