text string | label int64 |
|---|---|
[CLS]
00401000 <.text>:
401000: 68 40 df 4e 00 push $0x4edf40
401005: e8 30 8b 0b 00 call 0x4b9b3a
40100a: 59 pop %ecx
40100b: c3 ret
40100c: cc int3
40100d: cc int3
40100e: cc int3
40100f: cc ... | 0 |
[CLS]
401189: 33 c5 xor %ebp,%eax
40118b: 50 push %eax
40118c: 8d 45 f4 lea -0xc(%ebp),%eax
40118f: 64 a3 00 00 00 00 mov %eax,%fs:0x0
401195: 8b f1 mov %ecx,%esi
401197: 89 75 f0 mov %esi,-0x10(%ebp)
40119a: 8b 4d 08 ... | 0 |
[CLS]
401367: 25 00 00 00 80 and $0x80000000,%eax
40136c: 89 75 d8 mov %esi,-0x28(%ebp)
40136f: 83 c9 07 or $0x7,%ecx
401372: c6 45 e8 00 movb $0x0,-0x18(%ebp)
401376: 0d 00 00 00 80 or $0x80000000,%eax
40137b: 81 c9 00 00 00 80 or $0x80000000,%e... | 0 |
[CLS]
401552: cc int3
401553: cc int3
401554: cc int3
401555: cc int3
401556: cc int3
401557: cc int3
401558: cc int3
401559: cc int3
40155a: cc int3... | 0 |
[CLS]
4016d3: 6a ff push $0xffffffff
4016d5: 68 30 f3 4c 00 push $0x4cf330
4016da: 64 a1 00 00 00 00 mov %fs:0x0,%eax
4016e0: 50 push %eax
4016e1: a1 bc 70 53 00 mov 0x5370bc,%eax
4016e6: 33 c5 xor %ebp,%eax
4016e8: 50 ... | 0 |
[CLS]
401896: cc int3
401897: cc int3
401898: cc int3
401899: cc int3
40189a: cc int3
40189b: cc int3
40189c: cc int3
40189d: cc int3
40189e: cc int3... | 0 |
[CLS]
401a67: 50 push %eax
401a68: 89 4d d4 mov %ecx,-0x2c(%ebp)
401a6b: 8d 4d d0 lea -0x30(%ebp),%ecx
401a6e: 57 push %edi
401a6f: e8 ac 08 00 00 call 0x402320
401a74: 8d 45 d0 lea -0x30(%ebp),%eax
401a77: c6 45 fc 02 ... | 0 |
[CLS]
401c6c: 8d 45 f4 lea -0xc(%ebp),%eax
401c6f: 64 a3 00 00 00 00 mov %eax,%fs:0x0
401c75: 8b d9 mov %ecx,%ebx
401c77: 8b 75 08 mov 0x8(%ebp),%esi
401c7a: 85 f6 test %esi,%esi
401c7c: 74 79 je 0x401cf7
401c7e: 66 90 ... | 0 |
"[CLS]\n401e66:\te9 13 01 00 00 \tjmp 0x401f7e\n401e6b:\t3b 46 08 \tcmp 0x8((...TRUNCATED) | 0 |
"[CLS]\n402017:\t57 \tpush %edi\n402018:\t8b f9 \tmov %ecx,%ed(...TRUNCATED) | 0 |
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
objdumpon 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 windowlabel: A binary classification label0= benign1= 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)
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