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Python Forensics provides many never-before-published proven forensic modules, libraries, and solutions that can be used right out of the box. In addition, detailed instruction and documentation provided with the code samples will allow even novice Python programmers to add their own unique twists or use the models presented to build new solutions. Rapid development of new cybercrime investigation tools is an essential ingredient in virtually every case and environment. Whether you are performing post-mortem investigation, executing live triage, extracting evidence from mobile devices or cloud services, or you are collecting and processing evidence from a network, Python forensic implementations can fill in the gaps. Drawing upon years of practical experience and using numerous examples and illustrative code samples, author Chet Hosmer discusses how to: Develop new forensic solutions independent of large vendor software release schedules Participate in an open-source workbench that facilitates direct involvement in the design and implementation of new methods that augment or replace existing tools Advance your career by creating new solutions along with the construction of cutting-edge automation solutions to solve old problems.
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This book reveals a revolutionary and open source method for exposing nefarious network activity. Hosmer shows how to effectively and definitively passively map networks. Active or probing methods to network mapping have traditionally been used, but they have many drawbacks - they can disrupt operations, crash systems, and - most importantly - miss critical nefarious activity. It provides new innovations to passive network mapping, while delivering open source Python-based tools that can be put into practice immediately. --
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Integrating Python with Leading Computer Forensic Platforms takes a definitive look at how and why the integration of Python advances the field of digital forensics. In addition, the book includes practical, never seen Python examples that can be immediately put to use. Noted author Chet Hosmer demonstrates how to extend four key Forensic Platforms using Python, including EnCase by Guidance Software, MPE+ by AccessData, The Open Source Autopsy/SleuthKit by Brian Carrier and WetStone Technologies, and Live Acquisition and Triage Tool US-LATT. This book is for practitioners, forensic investigators, educators, students, private investigators, or anyone advancing digital forensics for investigating cybercrime. Additionally, the open source availability of the examples allows for sharing and growth within the industry. This book is the first to provide details on how to directly integrate Python into key forensic platforms.
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As data hiding detection and forensic techniques have matured, people are creating more advanced stealth methods for spying, corporate espionage, terrorism, and cyber warfare all to avoid detection. Data Hiding provides an exploration into the present day and next generation of tools and techniques used in covert communications, advanced malware methods and data concealment tactics. The hiding techniques outlined include the latest technologies including mobile devices, multimedia, virtualization and others. These concepts provide corporate, goverment and military personnel with the knowledge to investigate and defend against insider threats, spy techniques, espionage, advanced malware and secret communications. By understanding the plethora of threats, you will gain an understanding of the methods to defend oneself from these threats through detection, investigation, mitigation and prevention. Review of the latest data hiding techniques presented at world-renownded conferencesProvides many real-world examples of data concealment on the latest technologies including iOS, Android, VMware, MacOS X, Linux and Windows 7Dives deep into the less known approaches to data hiding, covert communications, and advanced malwareIncludes never before published information about next generation methods of data hidingOutlines a well-defined methodology for countering threats. Looks ahead at future predictions for data hiding.
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