Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/oalieno/asm2vec-pytorch
Unofficial implementation of asm2vec using pytorch ( with GPU acceleration )
https://github.com/oalieno/asm2vec-pytorch
asm2vec gpu-acceleration machine-learning neural-language-processing python pytorch unofficial
Last synced: 3 months ago
JSON representation
Unofficial implementation of asm2vec using pytorch ( with GPU acceleration )
- Host: GitHub
- URL: https://github.com/oalieno/asm2vec-pytorch
- Owner: oalieno
- License: mit
- Created: 2021-01-31T10:21:51.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2023-10-25T16:06:26.000Z (8 months ago)
- Last Synced: 2024-01-20T04:48:05.090Z (5 months ago)
- Topics: asm2vec, gpu-acceleration, machine-learning, neural-language-processing, python, pytorch, unofficial
- Language: Python
- Homepage:
- Size: 60.5 KB
- Stars: 65
- Watchers: 2
- Forks: 19
- Open Issues: 8
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-asm2vec - Asm2Vec-Pytorch
README
# asm2vec-pytorch
Unofficial implementation of `asm2vec` using pytorch ( with GPU acceleration )
The details of the model can be found in the original paper: [(sp'19) Asm2Vec: Boosting Static Representation Robustness for Binary Clone Search against Code Obfuscation and Compiler Optimization](https://www.computer.org/csdl/proceedings-article/sp/2019/666000a038/19skfc3ZfKo)
## Requirements
python >= 3.6
| packages | for |
| --- | --- |
| r2pipe | `scripts/bin2asm.py` |
| click | `scripts/*` |
| torch | almost all code need it |
You also need to install `radare2` to run `scripts/bin2asm.py`. `r2pipe` is just the python interface to `radare2`
If you only want to use the library code, you just need to install `torch`
## Install
```
python setup.py install
```
or
```
pip install git+https://github.com/oalieno/asm2vec-pytorch.git
```
## Benchmark
An implementation already exists here: [Lancern/asm2vec](https://github.com/Lancern/asm2vec)
Following is the benchmark of training 1000 functions in 1 epoch.
| Implementation | Time (s) |
| :-: | :-: |
| [Lancern/asm2vec](https://github.com/Lancern/asm2vec) | 202.23 |
| [oalieno/asm2vec-pytorch](https://github.com/oalieno/asm2vec-pytorch) (with CPU) | 9.11 |
| [oalieno/asm2vec-pytorch](https://github.com/oalieno/asm2vec-pytorch) (with GPU) | 0.97 |
## Get Started
```bash
python scripts/bin2asm.py -i /bin/ -o asm/
```
First generate asm files from binarys under `/bin/`.
You can hit `Ctrl+C` anytime when there is enough data.
```bash
python scripts/train.py -i asm/ -l 100 -o model.pt --epochs 100
```
Try to train the model using only 100 functions and 100 epochs for a taste.
Then you can use more data if you want.
```bash
python scripts/test.py -i asm/123456 -m model.pt
```
After you train your model, try to grab an assembly function and see the result.
This script will show you how the model perform.
Once you satisfied, you can take out the embedding vector of the function and do whatever you want with it.
## Usage
### bin2asm.py
```
Usage: bin2asm.py [OPTIONS]
Extract assembly functions from binary executable
Options:
-i, --input TEXT input directory / file [required]
-o, --output TEXT output directory
-l, --len INTEGER ignore assembly code with instructions amount smaller
than minlen
--help Show this message and exit.
```
```bash
# Example
python bin2asm.py -i /bin/ -o asm/
```
### train.py
```
Usage: train.py [OPTIONS]
Options:
-i, --input TEXT training data folder [required]
-o, --output TEXT output model path [default: model.pt]
-m, --model TEXT load previous trained model path
-l, --limit INTEGER limit the number of functions to be loaded
-d, --ebedding-dimension INTEGER
embedding dimension [default: 100]
-b, --batch-size INTEGER batch size [default: 1024]
-e, --epochs INTEGER training epochs [default: 10]
-n, --neg-sample-num INTEGER negative sampling amount [default: 25]
-a, --calculate-accuracy whether calculate accuracy ( will be
significantly slower )
-c, --device TEXT hardware device to be used: cpu / cuda /
auto [default: auto]
-lr, --learning-rate FLOAT learning rate [default: 0.02]
--help Show this message and exit.
```
```bash
# Example
python train.py -i asm/ -o model.pt --epochs 100
```
### test.py
```
Usage: test.py [OPTIONS]
Options:
-i, --input TEXT target function [required]
-m, --model TEXT model path [required]
-e, --epochs INTEGER training epochs [default: 10]
-n, --neg-sample-num INTEGER negative sampling amount [default: 25]
-l, --limit INTEGER limit the amount of output probability result
-c, --device TEXT hardware device to be used: cpu / cuda / auto
[default: auto]
-lr, --learning-rate FLOAT learning rate [default: 0.02]
-p, --pretty pretty print table [default: False]
--help Show this message and exit.
```
```bash
# Example
python test.py -i asm/123456 -m model.pt
```
```
┌──────────────────────────────────────────┐
│ endbr64 │
│ ➔ push r15 │
│ push r14 │
├────────┬─────────────────────────────────┤
│ 34.68% │ [rdx + rsi*CONST + CONST] │
│ 20.29% │ push │
│ 16.22% │ r15 │
│ 04.36% │ r14 │
│ 03.55% │ r11d │
└────────┴─────────────────────────────────┘
```
### compare.py
```
Usage: compare.py [OPTIONS]
Options:
-i1, --input1 TEXT target function 1 [required]
-i2, --input2 TEXT target function 2 [required]
-m, --model TEXT model path [required]
-e, --epochs INTEGER training epochs [default: 10]
-c, --device TEXT hardware device to be used: cpu / cuda / auto
[default: auto]
-lr, --learning-rate FLOAT learning rate [default: 0.02]
--help Show this message and exit.
```
```bash
# Example
python compare.py -i1 asm/123456 -i2 asm/654321 -m model.pt -e 30
```
```
cosine similarity : 0.873684
```