Python Scripting For Network Engineers Pdf – One of the most common formats for data is PDF. Invoices, reports and other forms are often stored by businesses and institutions in Portable Document Format (PDF) files.
Extracting data from PDF files can be cumbersome and time consuming. Fortunately, Python provides several libraries for easy data extraction from PDF files.
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This tutorial will explain how to extract data from PDF files using Python. You will learn how to install the necessary libraries and I will give examples of how to do this.
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There are many Python libraries you can use to read and extract data from PDF files. These include PDFMiner, PyPDF2, PDFQuery and PyMuPDF. Here we will use PDFQuery to read and extract data from multiple PDF files.
PDFQuery is a Python library that provides a simple way to extract data from PDF files using CSS-like selectors to find elements in the document.
It reads a PDF file as an object, converts the PDF object to an XML file, and accesses the requested information from its specific location within the PDF document.
In this code, we first create a PDFQuery object by passing the filename of the PDF file we want to get data from. Then we load the document into the object by calling it.
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Let’s consider another method we can use to read PDF files, extract some data items, and create a structured dataset using PDFQuery. We will follow the steps below:
We will read the PDF file and load it as an element object in our project. Convert a PDF object to an Extensible Markup Language (XML) file. This file contains the data and metadata of a particular PDF page.
XML defines a set of rules for encoding the PDF in a human and machine readable format. When we look at the XML file using a text editor, we can see where the data we want to extract is located.
Text box coordinates. You can think of it as the boundaries around the data we want to extract.
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Note: Sometimes the data we want to extract may not be in the exact same location in every file, which can cause problems. Fortunately, PDFQuery can also query tags containing a specific string.
Extracting data from PDF files is an important task as these files are often used for document storage and sharing.
Python’s PDFQuery is a powerful tool for extracting data from PDF files. Anyone looking to extract data from PDF files will find PDFQuery to be an excellent choice, thanks to its simple syntax and extensive documentation. It is also open source and can be modified to suit certain use cases.
Hello 👋 My name is Shittu Olumide; I am a passionate software engineer and technical writer for the community and its members.
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Learn to code for free. K’s open source courses have helped more than 40,000 people find jobs as developers. Getting Started It is becoming increasingly clear that security is an important aspect of IT infrastructure. A data breach is a major security incident, usually by hacking a simple network line. Increasing the security of your network helps strengthen your defenses against cyber attacks. Meanwhile, Python is being used for more and more advanced tasks, with the latest updates introducing a number of new packages. This book focuses on leveraging these updated packages to create a secure network with the help of Python scripting.
The book covers topics ranging from building a network to the various processes you need to follow to secure it. You will be introduced to the various packages and libraries before moving on to the various ways of creating a network with the help of Python scripting. Next, you’ll learn how to search for network vulnerabilities using the Python security script, and you’ll understand how to search for vulnerabilities in your network. As you progress through the chapters, you’ll learn how to write forensic scripts as well as how to leverage Python packages for endpoint protection. By the end of this book, you will be able to get the most out of the Python language to build secure and robust networks that are resistant to attacks.
This book is ideal for network engineers, system administrators, or any security professional dealing with networking and security issues. Programmers with prior Python experience will get the most out of this book. Common programming constructs and some basic knowledge of Python are required.
Identifying server vulnerabilities in web applications This section covers the main vulnerabilities in web applications and the tools we can find in the python ecosystem such as w3af as a scanner for vulnerabilities in web applications and SQL vulnerabilities to monitor.sqlmap. Regarding server vulnerabilities, we cover testing for Heartbleed and SSL vulnerabilities on openssl enabled servers. This chapter will cover the following topics: Vulnerabilities in web applications Vulnerabilities in web applications with OWASP w3af as the scanner How to discover SQL vulnerabilities with Python tools Python scripts to test HEART and SSL/TLS vulnerabilities Technical requirements Examples and source code for this chapter Chapter on GitHub repository It is available in folder 11: https://github.com/PacktPublishing/Mastering-Python-for-Networking-and-Security You will need at least 4GB of memory on your local machine. Python distribution. The scripts can be run with Python versions 2.7 and 3.x and have been tested on Unix distributions such as w3af, Ubuntu. Introduction to Vulnerabilities in Web Applications with OWASP The Open Web Application Security Project (OWASP) Top 10 is a list of the top 10 web application security risks. In this section, we’ll comment on OWASP’s top 10 vulnerabilities and detail cross-site scripting (XSS) vulnerabilities. Introduction to OWASP The Open Web Application Security Project is an excellent resource for learning ways to protect your web applications from bad behavior. There are many types of application vulnerabilities. OWASP has listed the top ten application security risks in the OWASP Top Ten Project: https://www.owasp.org/index.php/Category:OWASP_Top_Ten_2017_Project. The full classification can be found in the shared Excel file OWASP.xlsx located in the GitHub repository inside the Sections folder: Here we can reveal the following codes: OTG-INFO-001 Information leak: We use search engines like Bing , Google and Shodan who leaked using operators or idiots provided by these search engines searching for information. For example, we can see what information Shodan gives us by doing an IP or domain search, and we can see the services and ports that Shodan has disclosed with its service. OTG-INFO-002 Web Server Fingerprint: We will try to find out what kind of server our target website is running on, for this we use whatweb tool that we can find in Kali Linux distribution. Metadata contained in OTG-INFO-003 Server files: At this point we can use a tool like Foca or Metagoofil to extract metadata from documents published on the website. OTG-INFO-004 Enumeration of subdomains and servlets: We will use tools that inform us about possible subdomains, DNS servers, services and ports opened in servlets. OTG-INFO-005 Web Comments and Metadata: We can find leak information in comments on the web that programmers use to debug code. OTG-INFO-006 and OTG-INFO-007 Define entry points and website map: We can track all endpoints of web entry (requests and responses with GET and POST) from which we can perform a reverse web. use proxy (ZAP, Burp or WebScarab) and its spider to create a complete map of the web and its entry points. OTG-INFO-008 Fingerprint Web Application Frameworks: It’s about figuring out what kind of frameworks are used to develop the web, eg programming languages and techniques. We can find all this information in HTTP headers, cookies, HTML code and various files and folders. When we used whatweb tools, we could see that JQuery uses other typical technologies used by CMS. OTG-INFO-009 Fingerprint web applications: It’s about detecting whether some type of CMS is used to develop the web: WordPress, Joomla or other type of CMS. OTG-INFO-0010 Server Architecture: We can check if there is any firewall between communication. For this to work, we can do some sort of port scan and see for example if there is a web application firewall causing port 80 to be unfiltered. OWASP Pervasive Attacks Let’s take a look at some of the most common attacks: SQL injection: Injection of SQL code happens when user-supplied data is sent to the interpreter without filtering to change the original behavior. Or arbitrary queries in the database. The attacker sends raw SQL statements in the request. If your server uses some request context to generate a SQL query, it can execute an attacker’s request to the database. But if you use SQLAlchemy in Python and avoid raw SQL statements altogether, you’re safe. If you’re using raw SQL, make sure each variable is quoted correctly. We can find more information and ovasp documentation on this species.
