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Home > Security > Cybersecurity > Python for Secret Agents - Volume II
Python for Secret Agents - Volume II
Python for Secret Agents - Volume II

Python for Secret Agents - Volume II: Gather, analyze, and decode data to reveal hidden facts using Python, the perfect tool for all aspiring secret agents , Second Edition

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Python for Secret Agents - Volume II

Chapter 2. Tracks, Trails, and Logs

In many cases, espionage is about data: primary facts and figures that help make an informed decision. It can be military, but it's more commonly economic or engineering in nature. Where's the best place to locate a new building? How well is the other team really doing? Among all of these prospects, which is the best choice?

In some cases, we're looking for data that's one step removed from the primary facts. We're might need to know who's downloading the current team statistics? Who's reading the press-release information? Who's writing the bulk of the comments in our comments section? Which documents are really being downloaded? What is the pattern of access?

We're going to get data about the users and sources of some primary data. It's commonly called metadata: data about the primary data. It's the lifeblood of counter-intelligence.

We'll get essential web server metadata first. We...

Background briefing – web servers and logs

At its heart, the World Wide Web is a vast collection of computers that handle the HTTP protocol. The HTTP protocol defines a request message and a response. A web server handles these requests, creating appropriate responses. This activity is written to a log, and we're interested in that log.

When we interact with a complex web site for a company that conducts e-business—buying or selling on the web—it can seem a lot more sophisticated than this simplistic request and reply protocol. This apparent complexity arises from an HTML web page, which includes JavaScript programming. This extra layer of code can make requests and process replies in ways that aren't obvious to the user of the site.

All web site processing begins with some initial request for an HTML web page. Other requests from JavaScript programs will be data requests that don't lead to a complete HTML page being sent from the server. It's common...

Writing a regular expression for parsing

The logs look complex. Here's a sample line from a log:

109.128.44.217 - - [31/May/2015:22:55:59 -0400] "GET / HTTP/1.1" 200 14376 "-" "Mozilla/5.0 (iPad; CPU OS 8_1_2 like Mac OS X) AppleWebKit/600.1.4 (KHTML, like Gecko) Version/8.0 Mobile/12B440 Safari/600.1.4"

How can we pick this apart? Python offers us regular expressions as a way to describe (and parse) this string of characters.

We write a regular expression as a way of defining a set of strings. The set can be very small and have only a single string in it, or the set can be large and describe an infinite number of related strings. We have two issues that we have to overcome: how do we specify infinite sets? How can we separate those characters that help specify a rule from characters that just mean themselves?

For example, we might write a regular expression like aabr. This specifies a set that contains a single string. This regular expression looks like the...

Reading and understanding the raw data

Files come in a variety of formats. Even a file that appears to be simple text is often a UTF-8 encoding of Unicode characters. When we're processing data to extract intelligence, we need to look at three tiers of representation:

  • Physical Format: We might have a text file encoded in UTF-8, or we might have a GZIP file, which is a compressed version of the text file. Across these different physical formats, we can find a common structure. In the case of log files, the common structure is a line of text which represents a single event.
  • Logical Layout: After we've extracted data from the physical form, we often find that the order of the fields is slightly different or some optional fields are missing. The trick of using named groups in a regular expression gives us a way to handle variations in the logical layouts by using different regular expressions depending on the details of the layout.
  • Conceptual Content: This is the data we were looking...

Reading remote files

We've given these functions names such as local_text and local_gzip because the files are located on our local machine. We might want to write other variations that use urrlib.request.urlopen() to open remote files. For example, we might have a log file on a remote server that we'd like to process. This allows us to write a generator function, which yields lines from a remote file allowing us to interleave processing and downloading in a single operation.

We can use the urllib.request module to handle remote files using URLs of this form: ftp://username:password@/server/path/to/file. We can also use URLs of the form file:///path/to/file to read local files. Because of this transparency, we might want to look at using urllib.request for all file access.

As a practical matter, it's somewhat more common to use FTP to acquire files in bulk.

Studying a log in more detail

A file is the serialized representation for Python objects. In some rare cases, the objects are strings, and we can deserialize the strings from the text file directly. In the case of our web server logs, some of the strings represent a date-time stamp. Also, the size of the transmitted content shouldn't be treated as a string, since it's properly either an integer size or the None object if nothing was transmitted to the browser.

When requests for analysis come in, we'll often have to convert objects from strings to more useful Python objects. Generally, we're happiest if we simply convert everything into a useful, native Python data structure.

What kind of data structure should we use? We can't continue to use a Match object: it only knows about strings. We want to work with integers and datetimes.

The first answer is often to create a customized class that will hold the various attributes from a single entry in a log. This gives the...

Background briefing – web servers and logs

At its heart, the World Wide Web is a vast collection of computers that handle the HTTP protocol. The HTTP protocol defines a request message and a response. A web server handles these requests, creating appropriate responses. This activity is written to a log, and we're interested in that log.

When we interact with a complex web site for a company that conducts e-business—buying or selling on the web—it can seem a lot more sophisticated than this simplistic request and reply protocol. This apparent complexity arises from an HTML web page, which includes JavaScript programming. This extra layer of code can make requests and process replies in ways that aren't obvious to the user of the site.

All web site processing begins with some initial request for an HTML web page. Other requests from JavaScript programs will be data requests that don't lead to a complete HTML page being sent from the server. It's common for JavaScript programs to request JSON...

Writing a regular expression for parsing

The logs look complex. Here's a sample line from a log:

109.128.44.217 - - [31/May/2015:22:55:59 -0400] "GET / HTTP/1.1" 200 14376 "-" "Mozilla/5.0 (iPad; CPU OS 8_1_2 like Mac OS X) AppleWebKit/600.1.4 (KHTML, like Gecko) Version/8.0 Mobile/12B440 Safari/600.1.4"

How can we pick this apart? Python offers us regular expressions as a way to describe (and parse) this string of characters.

We write a regular expression as a way of defining a set of strings. The set can be very small and have only a single string in it, or the set can be large and describe an infinite number of related strings. We have two issues that we have to overcome: how do we specify infinite sets? How can we separate those characters that help specify a rule from characters that just mean themselves?

For example, we might write a regular expression like aabr. This specifies a set that contains a single string. This regular expression looks like the mathematical expression a×a×b×r that...

Reading and understanding the raw data

Files come in a variety of formats. Even a file that appears to be simple text is often a UTF-8 encoding of Unicode characters. When we're processing data to extract intelligence, we need to look at three tiers of representation:

  • Physical Format: We might have a text file encoded in UTF-8, or we might have a GZIP file, which is a compressed version of the text file. Across these different physical formats, we can find a common structure. In the case of log files, the common structure is a line of text which represents a single event.

  • Logical Layout: After we've extracted data from the physical form, we often find that the order of the fields is slightly different or some optional fields are missing. The trick of using named groups in a regular expression gives us a way to handle variations in the logical layouts by using different regular expressions depending on the details of the layout.

  • Conceptual Content: This is the data we were looking for, represented...

Reading remote files

We've given these functions names such as local_text and local_gzip because the files are located on our local machine. We might want to write other variations that use urrlib.request.urlopen() to open remote files. For example, we might have a log file on a remote server that we'd like to process. This allows us to write a generator function, which yields lines from a remote file allowing us to interleave processing and downloading in a single operation.

We can use the urllib.request module to handle remote files using URLs of this form: ftp://username:password@/server/path/to/file. We can also use URLs of the form file:///path/to/file to read local files. Because of this transparency, we might want to look at using urllib.request for all file access.

As a practical matter, it's somewhat more common to use FTP to acquire files in bulk.

Studying a log in more detail

A file is the serialized representation for Python objects. In some rare cases, the objects are strings, and we can deserialize the strings from the text file directly. In the case of our web server logs, some of the strings represent a date-time stamp. Also, the size of the transmitted content shouldn't be treated as a string, since it's properly either an integer size or the None object if nothing was transmitted to the browser.

When requests for analysis come in, we'll often have to convert objects from strings to more useful Python objects. Generally, we're happiest if we simply convert everything into a useful, native Python data structure.

What kind of data structure should we use? We can't continue to use a Match object: it only knows about strings. We want to work with integers and datetimes.

The first answer is often to create a customized class that will hold the various attributes from a single entry in a log. This gives the most flexibility. It may...

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Key benefits

  • • Discover the essential features of Python programming: statements, variables, expressions, and many of the built-in data types
  • • Use Python’s standard library to do more sophisticated data gathering and analysis
  • • Written by a Python programming expert, with over 35 years' experience as a consultant, teacher, author and software developer

Description

Python is easy to learn and extensible programming language that allows any manner of secret agent to work with a variety of data. Agents from beginners to seasoned veterans will benefit from Python's simplicity and sophistication. The standard library provides numerous packages that move beyond simple beginner missions. The Python ecosystem of related packages and libraries supports deep information processing. This book will guide you through the process of upgrading your Python-based toolset for intelligence gathering, analysis, and communication. You'll explore the ways Python is used to analyze web logs to discover the trails of activities that can be found in web and database servers. We'll also look at how we can use Python to discover details of the social network by looking at the data available from social networking websites. Finally, you'll see how to extract history from PDF files, which opens up new sources of data, and you’ll learn about the ways you can gather data using an Arduino-based sensor device.

Who is this book for?

This book is for Secret Agents who have some exposure to Python. Our focus is on the Field Agents who are ready to do more sophisticated and complex programming in Python. We'll stick to simple statistics for the most part. A steady hand with a soldering iron is not required, but a skilled field agent should be able to assemble a working Arduino circuit to gather their own sensor data.

What you will learn

  • • Upgrade Python to the latest version and discover its latest and greatest tools
  • • Use Python libraries to extract data from log files that are designed more for people to read than for automated analysis
  • • Summarize log files and extract meaningful information
  • • Gather data from social networking sites and leverage your experience of analyzing log files to summarize the data you find
  • • Extract text and images from social networking sites
  • • Parse the complex and confusing data structures in a PDF file to extract meaningful text that we can analyze
  • • Connect small, intelligent devices to our computer to use them as remote sensors
  • • Use Python to analyze measurements from sensors to calibrate them and use sensors efficiently
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 08, 2015
Length: 180 pages
Edition : 2nd
Language : English
ISBN-13 : 9781785283406
Category :
Security
Languages :
Python
Concepts :
Cybersecurity

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Publication date : Dec 08, 2015
Length: 180 pages
Edition : 2nd
Language : English
ISBN-13 : 9781785283406
Category :
Security
Languages :
Python
Concepts :
Cybersecurity

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Python for Secret Agents
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Python for Secret Agents - Volume II
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Table of Contents

6 Chapters
1. New Missions – New Tools Chevron down icon Chevron up icon
1. New Missions – New Tools
Background briefing on tools
Background briefing: review of the Python language
Mission One – upgrade Beautiful Soup
Doing other upgrades
Mission to expand our toolkit
Summary
2. Tracks, Trails, and Logs Chevron down icon Chevron up icon
2. Tracks, Trails, and Logs
Background briefing – web servers and logs
Writing a regular expression for parsing
Reading and understanding the raw data
Reading remote files
Studying a log in more detail
What are they downloading?
Trails of activity
Who is this person?
Getting logs from a server with ftplib
Summary
3. Following the Social Network Chevron down icon Chevron up icon
3. Following the Social Network
Background briefing – images and social media
Who's doing the talking?
What do they seem to be talking about?
What are they posting?
Deep Under Cover – NLTK and language analysis
Summary
4. Dredging up History Chevron down icon Chevron up icon
4. Dredging up History
Background briefing–Portable Document Format
Extracting PDF content
Getting text data from a document
Understanding tables and complex layouts
Summary
5. Data Collection Gadgets Chevron down icon Chevron up icon
5. Data Collection Gadgets
Background briefing: Arduino basics
Starting with the digital output pins
Mastering the Arduino programming language
Seeing a better blinking light
Simple Arduino sensor data feed
Collecting analog data
Collecting bulk data with the Arduino
Data modeling and analysis with Python
Reducing noise with a simple filter
Solving problems adding an audible alarm
Summary
Index Chevron down icon Chevron up icon
Index

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About the author

Profile icon Steven F. Lott
Steven F. Lott
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Steven Lott has been programming since computers were large, expensive, and rare. Working for decades in high tech has given him exposure to a lot of ideas and techniques, some bad, but most are helpful to others. Since the 1990s, Steven has been engaged with Python, crafting an array of indispensable tools and applications. His profound expertise has led him to contribute significantly to Packt Publishing, penning notable titles like "Mastering Object-Oriented," "The Modern Python Cookbook," and "Functional Python Programming." A self-proclaimed technomad, Steven's unconventional lifestyle sees him residing on a boat, often anchored along the vibrant east coast of the US. He tries to live by the words “Don't come home until you have a story.”
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The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela