---
license: mit
task_categories:
- text-generation
- feature-extraction
language:
- en
tags:
- system-calls
- strace
- linux
- syscalls
- operating-systems
- code
pretty_name: Linux Strace Traces Dataset
size_categories:
- n<1K
---

# Linux Strace Traces Dataset

A comprehensive collection of strace traces from common Linux utilities, designed for training AI models on system call patterns, program behavior analysis, and operating system interactions.

## Dataset Description

This dataset contains **357 strace traces** captured from 119 different Linux command-line utilities, with each utility executed 3 times to capture runtime variations. Each trace includes detailed system call information with timestamps, arguments, and execution times.

### Dataset Statistics

- **Total traces**: 357 (119 unique commands × 3 runs each)
- **Categories**: 8
- **Format**: Parquet (Hugging Face native)
- **Raw size**: ~40 MB
- **Compressed size**: ~5 MB (Parquet)
- **License**: MIT

### Runtime Diversity

Each command was executed 3 times to capture natural variations in program execution:
- Different process IDs (PIDs)
- Different memory addresses (ASLR randomization)
- Different timestamps with microsecond precision
- Slight variations in syscall timing

This diversity helps train more robust AI models that can generalize across different execution contexts.

### Categories

1. **File I/O Operations** (75 traces = 25 commands × 3 runs)
   - File reading: `cat`, `head`, `tail`, `less`
   - File manipulation: `cp`, `mv`, `touch`, `rm`
   - Directory operations: `mkdir`, `rmdir`, `find`
   - File information: `stat`, `file`, `ls`, `du`, `df`

2. **Text Processing** (69 traces = 23 commands × 3 runs)
   - Pattern matching: `grep` (simple, case-insensitive, recursive, count)
   - Text transformation: `sed`, `awk`, `tr`
   - Sorting & filtering: `sort`, `uniq`, `cut`, `paste`
   - Analysis: `wc`, `diff`, `cmp`

3. **Process & System Info** (51 traces = 17 commands × 3 runs)
   - Process listing: `ps`, `top`, `pgrep`
   - System information: `uptime`, `who`, `whoami`, `id`, `uname`, `hostname`
   - Environment: `env`, `printenv`
   - Time: `date`, `cal`
   - Resources: `free`, `vmstat`

4. **Networking** (33 traces = 11 commands × 3 runs)
   - Network requests: `ping`, `curl`, `wget`
   - DNS queries: `host`, `dig`, `nslookup`
   - Network info: `ip`, `ss`, `netstat`

5. **Compression & Archives** (36 traces = 12 commands × 3 runs)
   - Tar operations: create, extract, list (with/without gzip)
   - Compression: `gzip`, `bzip2`
   - Zip operations: `zip`, `unzip`

6. **Scripting & Execution** (45 traces = 15 commands × 3 runs)
   - Shell execution: `bash`, `sh`
   - Python execution: scripts and inline commands
   - Utilities: `sleep`, `echo`, `printf`, `yes`, `seq`
   - Tests: `true`, `false`, `test`, `expr`

7. **Permissions & Ownership** (18 traces = 6 commands × 3 runs)
   - Permission modification: `chmod` (numeric, symbolic, recursive)
   - Permission viewing: `ls -l`
   - Umask operations

8. **Data Operations** (30 traces = 10 commands × 3 runs)
   - Random data: `dd` with `/dev/urandom` and `/dev/zero`
   - Encoding: `base64` (encode/decode)
   - Hashing: `md5sum`, `sha1sum`, `sha256sum`
   - Binary analysis: `hexdump`, `od`, `strings`

## Dataset Structure

### Data Fields

- `id` (string): Unique identifier for the trace (e.g., "cat_simple_run1")
- `category` (string): Category of the utility (e.g., "file_io")
- `command` (string): Full command executed (e.g., "cat test.txt")
- `strace` (string): Complete strace output with system calls
- `duration` (float): Execution time in seconds
- `timestamp` (string): ISO 8601 timestamp of trace capture
- `num_lines` (int): Number of lines in the strace output
- `size_bytes` (int): Size of the strace output in bytes
- `run_number` (int): Run iteration (1, 2, or 3)

### Data Splits

This dataset contains a single split:
- `train`: 357 examples (119 unique commands × 3 runs)

### Example

```python
from datasets import load_dataset

dataset = load_dataset("YOUR_USERNAME/strace-dataset")

# View first example
example = dataset['train'][0]
print(f"Command: {example['command']}")
print(f"Category: {example['category']}")
print(f"Duration: {example['duration']}s")
print(f"Strace output (first 500 chars):\n{example['strace'][:500]}")
```

## Dataset Creation

### Source Data

All traces were generated in a controlled, isolated environment using the `strace` utility with the following flags:
- `-f`: Follow child processes
- `-s 256`: Capture up to 256 bytes of string arguments
- `-tt`: Absolute timestamps with microsecond precision
- `-T`: Show time spent in each syscall

### Data Collection Process

1. Temporary workspace created with test files (text, binary, CSV, etc.)
2. Each utility executed with typical arguments/use cases
3. Strace captured all system calls during execution
4. Test data cleaned up after collection
5. No sensitive information or system-specific paths included

### Safety & Privacy

- ✅ No sensitive data (passwords, keys, tokens)
- ✅ No user-specific information
- ✅ No production system paths
- ✅ All operations performed in temporary directories
- ✅ Safe for open-source release

## Use Cases

### Training AI Models

- **System call prediction**: Predict next system calls based on program behavior
- **Anomaly detection**: Identify unusual syscall patterns
- **Program behavior modeling**: Learn normal execution patterns
- **Security analysis**: Detect potential malicious behavior

### Analysis & Research

- Understanding program execution flows
- Comparing syscall patterns across utilities
- Educational resource for OS concepts
- Benchmark for trace analysis tools

### Code Examples

#### Load and explore the dataset

```python
from datasets import load_dataset
import pandas as pd

# Load dataset
ds = load_dataset("andrew000/straces")

# Convert to pandas for analysis
df = pd.DataFrame(ds['train'])

# Group by category
print(df.groupby('category').size())

# Find longest-running commands
print(df.nlargest(5, 'duration')[['command', 'duration']])

# Analyze strace patterns
for example in ds['train']:
    if 'openat' in example['strace']:
        print(f"{example['command']} opens files")
```

#### Extract specific syscalls

```python
import re

def extract_syscalls(strace_output):
    """Extract syscall names from strace output."""
    pattern = r'\d+\s+[\d:.]+\s+(\w+)\('
    return list(set(re.findall(pattern, strace_output)))

# Find all syscalls used by cat
cat_example = [ex for ex in ds['train'] if ex['id'] == 'cat_simple'][0]
syscalls = extract_syscalls(cat_example['strace'])
print(f"cat uses these syscalls: {syscalls}")
```

## Limitations

- Traces captured on a specific Linux system (kernel version may affect syscalls)
- Limited to common command-line utilities
- No multi-threaded or complex concurrent workloads
- Dataset size: 357 traces (medium-sized dataset)

## Future Work

- Add more utilities and use cases
- Include long-running processes
- Multi-threaded application traces
- Different input sizes for scalability analysis
- Traces from different Linux distributions/kernels

## Citation

If you use this dataset in your research, please cite:

```bibtex
@dataset{strace_traces_2025,
  title={Linux Strace Traces Dataset},
  author={Andrew Fasano},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/datasets/andrew000/straces}}
}
```

## License

MIT License - See LICENSE file for details

## Acknowledgments

Generated using the `strace` utility on Linux. All traces are from standard open-source utilities.