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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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Profile Icon Steven F. Lott
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Profile Icon Steven F. Lott
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Paperback Dec 2015 180 pages 2nd 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 : Dec 08, 2015
Length: 180 pages
Edition : 2nd
Language : English
ISBN-13 : 9781785283406
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ISBN-13 : 9781785283406
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Table of Contents

6 Chapters
1. New Missions – New Tools Chevron down icon Chevron up icon
2. Tracks, Trails, and Logs Chevron down icon Chevron up icon
3. Following the Social Network Chevron down icon Chevron up icon
4. Dredging up History Chevron down icon Chevron up icon
5. Data Collection Gadgets Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Is it because they are all secret agent?
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