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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. 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 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/), specifically focusing on samples uploaded in **2020**. Each file was disassembled into assembly code using the `objdump` command on Ubuntu. |
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| - **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. |
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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: |
| - **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. |
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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 sliding window |
| - `label`: A binary classification label indicating `malware` or `benign` |
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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., Transformer-based models) |
| - Research on malware detection using sliding window context segmentation |
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| ## Tools Used |
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| - `objdump` (for extracting assembly code) |
| - PowerShell (for collecting benign files) |
| - Python scripts (for sliding window segmentation and maintaining per-file chunk structure) |
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