Socket Programming in Python

Socket programming is a way of connecting two nodes on a network to communicate with each other. One socket(node) listens on a particular port at an IP, while other socket reaches out to the other to form a connection. Server forms the listener socket while client reaches out to the server.They are the real backbones behind web browsing. In simpler terms there is a server and a client.

import socket
s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

Here we made a socket instance and passed it two parameters. The first parameter is AF_INET and the second one is SOCK_STREAM. AF_INET refers to the address family ipv4. The SOCK_STREAM means connection oriented TCP protocol.

Now we can connect to a server using this socket.

Connecting to a server:
Note that if any error occurs during the creation of a socket then a socket.error is thrown and we can only connect to a server by knowing it’s ip. You can find the ip of the server by using this :

$ ping www.google.com
import socket 

ip = socket.gethostbyname('www.google.com')
print ip

#Example of a SCRIPT

# An example script to connect to Google using socket

# programming in Python

import socket # for socket

import sys 

 

try:

    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

    print "Socket successfully created"

except socket.error as err:

    print "socket creation failed with error %s" %(err)

 

# default port for socket

port = 80

 

try:

    host_ip = socket.gethostbyname('www.google.com')

except socket.gaierror:

 

    # this means could not resolve the host

    print "there was an error resolving the host"

    sys.exit()

 

# connecting to the server

s.connect((host_ip, port))

 

print "the socket has successfully connected to google \

on port == %s" %(host_ip)







Output :
Socket successfully created
the socket has successfully connected to google 
on port == 173.194.40.19


First of all we made a socket.
Then we resolved google’s ip and lastly we connected to google.
Now we need to know how can we send some data through a socket.
For sending data the socket library has a sendall function. This function allows you to send data to a server to which the socket is connected and server can also send data to the client using this function.


A simple server-client program :

Server :
A server has a bind() method which binds it to a specific ip and port so that it can listen to incoming requests on that ip and port.A server has a listen() method which puts the server into listen mode. This allows the server to listen to incoming connections. And last a server has an accept() and close() method. The accept method initiates a connection with the client and the close method closes the connection with the client.





# first of all import the socket library

import socket               

 

# next create a socket object

s = socket.socket()         

print "Socket successfully created"

 

# reserve a port on your computer in our

# case it is 12345 but it can be anything

port = 12345               

 

# Next bind to the port

# we have not typed any ip in the ip field

# instead we have inputted an empty string

# this makes the server listen to requests 

# coming from other computers on the network

s.bind(('', port))        

print "socket binded to %s" %(port)

 

# put the socket into listening mode

s.listen(5)     

print "socket is listening"           

 

# a forever loop until we interrupt it or 

# an error occurs

while True:

 

   # Establish connection with client.

   c, addr = s.accept()     

   print 'Got connection from', addr

 

   # send a thank you message to the client. 

   c.send('Thank you for connecting')

 

   # Close the connection with the client

   c.close()





Reference : Python Socket Programming

If you like Abin's blog and would like to contribute, you can also write an article using abinabraham.online or mail your article to ambattuabin@gmail.com. 

Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.

This bug let a researcher bypass GoDaddy's site security tool

A widely-used security tool owned by web hosting provider GoDaddy, designed to prevent websites from being hacked, was easily bypassed, putting websites at risk of data theft.

The company's website application firewall (WAF), provided by Sucuri and acquired by GoDaddy earlier this year, protects websites against a range of attacks by adding an extra layer of security to a website to protect against cross-site scripting and SQL injection techniques.

But a security researcher told ZDNet that the firewall would let through some commands, allowing him to gain access to vulnerable databases behind the scenes. That, he said, put sites at risk of data theft.

Touseef Gul was able to bypass the firewall with a relatively simple SQL injection string, which he showed to ZDNet but we're not publishing. SQL injection attacks can be launched from the web browser's address bar. If the attack is successful it will display a list of database tables on the website itself. Where he was expecting to receive an "access denied" message, the firewall let the command through and returned a list of tables from the target website's database. He was also able to obtain the database's admin account and MD5 hashed password, which nowadays is easily crackable.

What surprised the researcher, he said, was how easy the firewall was to bypass.

He gave an example of part of the code he used. He said that while the firewall would block a common command used in SQL injections, such as "UNION SELECT," a modified, encoded version of the same command -- such as "UNION SELE%63T" (where %63 is an encoded "C") -- was not blocked by the filter.

For its part, GoDaddy said it patched the bug within a day of the security researcher's private disclosure to the company.

"In reviewing this situation, it appears someone was able to find a vulnerable website and manipulate their requests to temporarily bypass our WAF," said Daniel Cid, GoDaddy's vice-president of engineering.

"Within less than a day, our systems were able to pick up this attempt and put a stop to it," he said.

Cid said the company is "not aware of other customers" impacted by the bypass, but wouldn't say how many websites were at risk of the bypass technique.

Lesley Carhart, a digital forensics and incident response specialist, explained that web application firewalls mimic the behavior of antivirus products rather than a traditional firewall.

"In a lot of ways web attacks are way harder to firewall than traffic in and out of a network," said Carhart. "You can deny almost everything at a network firewall or host firewall."

"Web traffic filtering relies more on blacklisting bad stuff using signatures than whitelisting slews of unneeded ports and protocols like traditional firewalls," she added.

Web application firewalls block attacks on sites running web applications that are already vulnerable to attacks, like out-of-date content management systems, like WordPress or Joomla, she explained.

x

Django REST Framework's Token Based Authentication

Do the following things BEFORE you create the superuser. Otherwise, the superuser does not get his/her token created.

 

INSTALLED_APPS = (
    'rest_framework',
    'rest_framework.authtoken',
    'myapp',
)

REST_FRAMEWORK = {
    'DEFAULT_PERMISSION_CLASSES': (
        'rest_framework.permissions.IsAuthenticated',
    ),
    'DEFAULT_AUTHENTICATION_CLASSES': (
        'rest_framework.authentication.TokenAuthentication',
    )
}

Alternatively, if you want to be more explicit, create a file named signals.py under myapp project. Put the code above in it, then in init.py, write import signals

Improvements

DRF's token implementation lacks a few important features:

• Tokens do not rotate
• Tokens do not expire
• The same token is shared among all the clients (PC browsers, smartphones, tablets, etc.)

The Django Oauth Toolkit should be considered as a step up.

from django.db.models.signals import post_save
from django.dispatch import receiver
from rest_framework.authtoken.models import Token
from django.conf import settings

# This code is triggered whenever a new user has been created and saved to the database

@receiver(post_save, sender=settings.AUTH_USER_MODEL)
def create_auth_token(sender, instance=None, created=False, **kwargs):
    if created:
        Token.objects.create(user=instance)

Caching in Django With Redis

Django’s cache framework

Django comes with a robust cache system that lets you save dynamic pages so they don’t have to be calculated for each request. For convenience, Django offers different levels of cache granularity: You can cache the output of specific views, you can cache only the pieces that are difficult to produce, or you can cache your entire site.

Django also works well with “downstream” caches, such as Squid and browser-based caches. These are the types of caches that you don’t directly control but to which you can provide hints (via HTTP headers) about which parts of your site should be cached, and how.

CACHES = { "default": { "BACKEND": "django_redis.cache.RedisCache", "LOCATION": "redis://127.0.0.1:6379/1", "OPTIONS": { "CLIENT_CLASS": "django_redis.client.DefaultClient" }, "KEY_PREFIX": "example" } }

 

Implement Caching

Imagine the total number of network calls that our application will make as users start to visit our site. If 1,000 users hit the API that retrieves cookbook recipes, then our application will query the database 3,000 times and a new template will be rendered with each request. That number only grows as our application scales. Luckily, this view is a great candidate for caching. The recipes in a cookbook rarely change, if ever. Also, since viewing cookbooks is the central theme of the app, the API retrieving the recipes is guaranteed to be called frequently.

In the example below, we modify the view function to use caching. When the function runs, it checks if the view key is in the cache. If the key exists, then the app retrieves the data from the cache and returns it. If not, Django queries the database and then stashes the result in the cache with the view key. The first time this function is run, Django will query the database and render the template, and then will also make a network call to Redis to store the data in the cache. Each subsequent call to the function will completely bypass the database and business logic and will query the Redis cache.

in settings.py

# Cache time to live is 15 minutes. CACHE_TTL = 60 * 15

Views can be update by

from django.conf import settings from django.core.cache.backends.base import DEFAULT_TIMEOUT from django.shortcuts import render from django.views.decorators.cache import cache_page from cookbook.services import get_recipes CACHE_TTL = getattr(settings, 'CACHE_TTL', DEFAULT_TIMEOUT) @cache_page(CACHE_TTL) def recipes_view(request): return render(request, 'cookbook/recipes.html', { 'recipes': get_recipes() })

Notice that we have added the @cache_page() decorator to the view function, along with a time to live. Visit the /cookbook/ URL again and examine the Django Debug Toolbar. We see that 3 database queries are made and 3 calls are made to the cache in order to check for the key and then to save it. Django saves two keys (1 key for the header and 1 key for the rendered page content). Reload the page and observe how the page activity changes. The second time around, 0 calls are made to the database and 2 calls are made to the cache. Our page is now being served from the cache!

¶

Python OrderedDict

An OrderedDict is a dictionary subclass that remembers the order in which its contents are added


 import collections

print 'Regular dictionary:'
d = {}
d['a'] = 'A'
d['b'] = 'B'
d['c'] = 'C'
d['d'] = 'D'
d['e'] = 'E'

for k, v in d.items():
    print k, v

print '\nOrderedDict:'
d = collections.OrderedDict()
d['a'] = 'A'
d['b'] = 'B'
d['c'] = 'C'
d['d'] = 'D'
d['e'] = 'E'

for k, v in d.items():
    print k, v


 A regular dict does not track the insertion order, and iterating over it produces the values in an arbitrary order. In an OrderedDict, by contrast, the order the items are inserted is remembered and used when creating an iterator.

 A regular dict looks at its contents when testing for equality. An OrderedDict also considers the order the items were added.

Touch Develop

Every 11 year-old in Britain will be getting the BBC Micro Bit this autumn. The platform will be programmable with three programming languages: Touch Develop (https://www.touchdevelop.com/), C++, and Python.

The Python Software Foundation is a supporting organization of this initiative. The PSF blog post on the topic can be found athttp://pyfound.blogspot.com/2015/03/bbc-launches-microbit.html

https://www.touchdevelop.com/

Docs

Blogs

Launch Editor 

Google's TensorFlow

TensorFlow is an Open Source Software Library for Machine Intelligence

About TensorFlow

TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google's Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.

What is a Data Flow Graph?

Data flow graphs describe mathematical computation with a directed graph of nodes & edges. Nodes typically implement mathematical operations, but can also represent endpoints to feed in data, push out results, or read/write persistent variables. Edges describe the input/output relationships between nodes. These data edges carry dynamically-sized multidimensional data arrays, or tensors. The flow of tensors through the graph is where TensorFlow gets its name. Nodes are assigned to computational devices and execute asynchronously and in parallel once all the tensors on their incoming edges becomes available.

Features

Deep Flexibility

TensorFlow isn't a rigid neural networks library. If you can express your computation as a data flow graph, you can use TensorFlow. You construct the graph, and you write the inner loop that drives computation. We provide helpful tools to assemble subgraphs common in neural networks, but users can write their own higher-level libraries on top of TensorFlow. Defining handy new compositions of operators is as easy as writing a Python function and costs you nothing in performance. And if you don't see the low-level data operator you need, write a bit of C++ to add a new one.

True Portability

TensorFlow runs on CPUs or GPUs, and on desktop, server, or mobile computing platforms. Want to play around with a machine learning idea on your laptop without need of any special hardware? TensorFlow has you covered. Ready to scale-up and train that model faster on GPUs with no code changes? TensorFlow has you covered. Want to deploy that trained model on mobile as part of your product? TensorFlow has you covered. Changed your mind and want to run the model as a service in the cloud? Containerize with Docker and TensorFlow just works.

Connect Research and Production

Gone are the days when moving a machine learning idea from research to product require a major rewrite. At Google, research scientists experiment with new algorithms in TensorFlow, and product teams use TensorFlow to train and serve models live to real customers. Using TensorFlow allows industrial researchers to push ideas to products faster, and allows academic researchers to share code more directly and with greater scientific reproducibility.

Connect Research and Production

Gone are the days when moving a machine learning idea from research to product require a major rewrite. At Google, research scientists experiment with new algorithms in TensorFlow, and product teams use TensorFlow to train and serve models live to real customers. Using TensorFlow allows industrial researchers to push ideas to products faster, and allows academic researchers to share code more directly and with greater scientific reproducibility.

Language Options

TensorFlow comes with an easy to use Python interface and a no-nonsense C++ interface to build and execute your computational graphs. Write stand-alone TensorFlow Python or C++ programs, or try things out in an interactive TensorFlow iPython notebook where you can keep notes, code, and visualizations logically grouped. This is just the start though -- we’re hoping to entice you to contribute SWIG interfaces to your favorite language -- be it Go, Java, Lua, JavaScript, or R.

Maximize Performance

Want to use every ounce of muscle in that workstation with 32 CPU cores and 4 GPU cards? With first-class support for threads, queues, and asynchronous computation, TensorFlow allows you to make the most of your available hardware. Freely assign compute elements of your TensorFlow graph to different devices, and let TensorFlow handle the copies.

Thanks.....

Sources: Officila Media, Google View TensorFlow,