semvec/pypi-packages huggingface dataset:
as3nt 1.1.0
Another Subdomain ENumeration Tool
Requires Python: >=3.6

![Version 1.1.0](http://img.shields.io/badge/version-v1.1.0-purple.svg)
![Python 3.8](http://img.shields.io/badge/python-3.8-yellow.svg)
[![GPL License](http://img.shields.io/badge/license-GPL%20License-blue.svg)](https://github.com/cinerieus/as3nt/blob/master/LICENSE)  

## As3nt
Another Subdomain ENumeration Tool - written in python to enumerate and enrich subdomains using passive OSINT.  

As3nt can target TLDs or subdomains. The enumeration uses; VirusTotal, HackerTarget, ThreatMiner, urlscan.io and crt.sh. Each subdomain IP is resolved using public DNS servers and the data is enriched using ipwhois and Shodan. As3nt currently outputs to terminal or csv. 

Along with using the tags from Shodan, this tool also adds it's own tags. Currently there are tags added based on certain HTML content found by Shodan like tomcat, fortinet, netscaler and pulse vpn. Also if any CVEs are found for an IP/Subdomain with a CVSS score >= 7.8, these assets are tagged to highlight a possible exploit. 

## Screenshots 
![As3nt_1](https://github.com/cinerieus/as3nt/blob/master/screenshots/as3nt_1.gif)  
![As3nt_2](https://github.com/cinerieus/as3nt/blob/master/screenshots/as3nt_2.gif)

## Installation 
1. Install:
  - with pip: `pip install as3nt`
  - from git (source): 
	- `git clone https://github.com/cinerieus/as3nt.git && cd as3nt/`  
	- `pip install -r requirements.txt`
	- `python ./as3nt/core.py` 
  - from git (releases):
	- `wget https://github.com/cinerieus/as3nt/archive/v1.1.0.tar.gz`
	- `pip install v1.1.0.tar.gz` 
2. Remember to check PATH if you installed in ~/.local/bin/ 
3. Profit! 

*For Shodan functionality set the environment variable 'SHODANKEY' with your API key. 

## Dependencies 
See [requirements.txt](https://github.com/cinerieus/as3nt/blob/master/requirements.txt) 

## Changelog
See [CHANGELOG.md](https://github.com/cinerieus/as3nt/blob/master/CHANGELOG.md) 

## Usage 
![usage](https://github.com/cinerieus/as3nt/blob/master/screenshots/usage.png)  
*If selected Shodan is rate-limited to 1 IP per second.

#### Examples:  
- Run all modules against 'example.com' and save results to csv:  
`as3nt -t example.com -11 -o results.csv`   
- Run against a subdomain:  
`as3nt -s -t subdomain.example.com -11 -o results.csv`  

## Thanks  
- Thanks to [aboul3la](https://github.com/aboul3la/) for the inspiration from [sublist3r](https://github.com/aboul3la/Sublist3r)
infinite-training 1.0.0
The python package for training ML models base on loss, metrics and timeout.
Requires Python: >=3.7

# Infinite Training

The python package for training ML models base on loss, metrics and timeout. We can create target for training or using Ctrl + C to interrupt the training session. This package can store the last weight and optimize weight for us to continue training after interrupting.

Using as example:
```python
"""
    Apply in example from https://www.tensorflow.org/datasets/keras_example
"""
import tensorflow_datasets as tfds
import tensorflow as tf
from infinite_training import InfinityTraining, Target

(ds_train, ds_test), ds_info = tfds.load(
    'mnist',
    split=['train', 'test'],
    shuffle_files=True,
    as_supervised=True,
    with_info=True,
)


def normalize_img(image, label):
    """Normalizes images: `uint8` -> `float32`."""
    return tf.cast(image, tf.float32) / 255., label

ds_train = ds_train.map(
    normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
ds_train = ds_train.cache()
ds_train = ds_train.shuffle(ds_info.splits['train'].num_examples)
ds_train = ds_train.batch(128)
ds_train = ds_train.prefetch(tf.data.AUTOTUNE)

ds_test = ds_test.map(
    normalize_img, num_parallel_calls=tf.data.AUTOTUNE)
ds_test = ds_test.batch(128)
ds_test = ds_test.cache()
ds_test = ds_test.prefetch(tf.data.AUTOTUNE)

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10)
])

it = InfinityTraining(model=model, target=Target(
    name="val_sparse_categorical_accuracy", smaller_is_better=False, target_value=0.98), timeout=100)
it.compile(optimizer=tf.keras.optimizers.Adam(0.001),
           loss=tf.keras.losses.SparseCategoricalCrossentropy(
               from_logits=True),
           metrics=[tf.keras.metrics.SparseCategoricalAccuracy()],)
it.train(ds_train, validation_data=ds_test)
it.show_result(ds_train)
```
pyppeteer-stealth 2.7.4
pyppeteer stealth

# pyppeteer-stealth

Transplanted from [puppeteer-extra-plugin-stealth](https://github.com/berstend/puppeteer-extra/tree/master/packages/puppeteer-extra-plugin-stealth), **Not perfect**.

## Install

```
$ pip install pyppeteer_stealth
```

## Usage

```python
import asyncio
from pyppeteer import launch
from pyppeteer_stealth import stealth

async def main():
    browser = await launch(headless=True)
    page = await browser.newPage()

    await stealth(page)  # <-- Here

    await page.goto("https://bot.sannysoft.com/")
    await browser.close()
asyncio.get_event_loop().run_until_complete(main())
```

## Args

```python
stealth(
  page: Page,
  run_on_insecure_origins: bool = False,
  languages: [str] = ["en-US", "en"],
  vendor: str = "Google Inc."
  user_agent: str = None,
  locale: str = "en-US,en",
  mask_linux: bool = True,
  webgl_vendor: str = "Intel Inc.",
  renderer: str = "Intel Iris OpenGL Engine",
  disabled_evasions: list = [],
)
```

List of valid evasion names to pass into `disabled_evasions`:
```python
['chrome_app',
 'chrome_runtime',
 'iframe_content_window',
 'media_codecs',
 'sourceurl',
 'navigator_hardware_concurrency',
 'navigator_languages',
 'navigator_permissions',
 'navigator_plugins',
 'navigator_vendor',
 'navigator_webdriver',
 'user_agent_override',
 'webgl_vendor',
 'window_outerdimensions']
```

## Test results

### Pyppeteer without stealth

<table class="image">
<tr>
  <td><figure class="image"><a href="https://github.com/MeiK2333/pyppeteer_stealth/blob/master/headless_without_stealth.png"><img src="https://github.com/MeiK2333/pyppeteer_stealth/blob/master/headless_without_stealth.png"></a><figcaption>headless</figcaption></figure></td>
  <td><figure class="image"><a href="https://github.com/MeiK2333/pyppeteer_stealth/blob/master/headful_without_stealth.png"><img src="https://github.com/MeiK2333/pyppeteer_stealth/blob/master/headful_without_stealth.png"></a><figcaption>headful</figcaption></figure></td>
</tr>
</table>

### Pyppeteer with stealth

<table class="image">
<tr>
  <td><figure class="image"><a href="https://github.com/MeiK2333/pyppeteer_stealth/blob/master/headless_with_stealth.png"><img src="https://github.com/MeiK2333/pyppeteer_stealth/blob/master/headless_with_stealth.png"></a><figcaption>headless</figcaption></figure></td>
  <td><figure class="image"><a href="https://github.com/MeiK2333/pyppeteer_stealth/blob/master/headful_with_stealth.png"><img src="https://github.com/MeiK2333/pyppeteer_stealth/blob/master/headful_with_stealth.png"></a><figcaption>headful</figcaption></figure></td>
</tr>
</table>
binary-inverter 0.0.1
Python script to invert all the bytes of an image
Requires Python: >=3.6
Keywords: bytes image image-manipulation crypting encoding binary

# binary-image-inverter
A simple script which will invert all the bytes of an image

## Usage
```
git clone https://ABFStudio/binary-image-inverter
cd binary-image-inverter
chmod +x ./binary_inverter.py
./binary_inverter <file-to-invert> <output-file>
```
If you want to revert the process, you can run the same command and invert files:  
```
./binary_inverter <inverted-file> <original-file>
```