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| license: mit |
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| # SMU\_MalwareDetection PE Assembly Dataset |
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| 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. |
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| ## ⚠️ Note: Class Imbalance |
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| 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. |
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| ## 🦠 Dataset Composition (Malware Families) |
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| The dataset is intentionally composed of samples from **three specific malware families** to support concept drift studies: |
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| * **RemcosRAT** (20 files) |
| * **AgentTesla** (45 files) |
| * **GuLoader** (20 files) |
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| Each malware family includes samples from various years to allow for temporal drift analysis. |
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| In total: |
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| * **85 malware PE files** |
| * **85 benign PE files** |
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| ## Dataset Description |
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| The dataset includes assembly code segments derived from two types of PE files: |
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| * **Malware Samples** |
| Malicious PE files were downloaded from [MalwareBazaar](https://bazaar.abuse.ch/), with a focus on samples uploaded in **multiple years**. Disassembly was performed using `objdump` on Ubuntu. |
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| * **Benign Samples** |
| Benign PE files were collected from trusted executable files found on a local machine using PowerShell, then disassembled similarly using `objdump`. |
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| ## Sliding Window Preprocessing |
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| To prepare the data for machine learning: |
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| * Assembly code was segmented using a **sliding window approach**: |
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| * **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. |
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| ## Data Format |
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| Each entry in the dataset includes: |
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| * `assembly`: A segment of assembly code extracted via the sliding window |
| * `label`: A binary classification label |
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| * `0` = benign |
| * `1` = malware |
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| ## Intended Use |
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| This dataset is suitable for the following tasks: |
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| * Malware classification using NLP-based models (e.g., Transformers) |
| * Research on **concept drift in malware detection** |
| * Context-aware malware analysis using segmented disassembly |
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| ## Tools Used |
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| * `objdump` (for disassembly) |
| * PowerShell (for benign file collection) |
| * Python (for segmentation and grouping) |
