Genticca/SMU_MalwareDetection_Year

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 and concept drift experiments across malware families and time periods. 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 Composition (Malware Families)

The dataset is intentionally composed of samples from three specific malware families to support concept drift studies:

• RemcosRAT (20 files)
• AgentTesla (45 files)
• GuLoader (20 files)

Each malware family includes samples from various years to allow for temporal drift analysis.

In total:

• 85 malware PE files
• 85 benign PE files

Dataset Description

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

• Malware Samples Malicious PE files were downloaded from MalwareBazaar, with a focus on samples uploaded in multiple years. Disassembly was performed using objdump on Ubuntu.

• Benign Samples Benign PE files were collected from trusted executable files found on a local machine using PowerShell, then disassembled similarly using objdump.

Sliding Window Preprocessing

To prepare the data for machine learning:

• Assembly code was segmented using a sliding window approach:

  • Window Size: 500 lines
  • Stride: 175 lines (overlap between windows)

• This method preserves contextual relationships between instructions.

• All segments generated from a single file are grouped together to avoid leakage between training and test sets.

• Raw assembly code is used as-is; no tokenization or preprocessing beyond segmentation was applied.

Data Format

Each entry in the dataset includes:

• assembly: A segment of assembly code extracted via the sliding window

• label: A binary classification label

  • 0 = benign
  • 1 = malware

Intended Use

This dataset is suitable for the following tasks:

• Malware classification using NLP-based models (e.g., Transformers)
• Research on concept drift in malware detection
• Context-aware malware analysis using segmented disassembly

Tools Used

• objdump (for disassembly)
• PowerShell (for benign file collection)
• Python (for segmentation and grouping)