Jonathan Zdziarski

iPhone Forensics: Performing a Live Recovery Over USB

Date: This event took place live on June 09 2009

Presented by: Jonathan Zdziarski

Duration: Approximately 60 minutes.

Cost: Free

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In this webcast, Jonathan Zdziarski, author of iPhone Forensics, will guide you through an overview of his latest technique to recover the live user disk from an iPhone. Jonathan will demonstrate how his existing technique, documented in the book, can be improved to transfer a disk image across USB in about 30 minutes, and without the need to bypass the iPhone passcode secuity or re-enable a disabled phone. You'll learn about Jonathan's new open source recovery agent and how this simplifies the process. If you have a copy of Jonthan's book, iPhone Forensics, you'll be able to follow along and learn where to apply these new techniques.

About Jonathan Zdziarski

Jonathan Zdziarski is better known as the hacker "NerveGas" in the iPhone development community. His work in cracking the iPhone helped lead the effort to port the first open source applications, and his book, iPhone Open Application Development, taught developers how to write applications for the popular device long before Apple introduced its own SDK. Prior to the release of iPhone Forensics, Jonathan wrote and supported an iPhone forensics manual distributed exclusively to law enforcement. Jonathan frequently consults law enforcement agencies and assists forensic examiners in their investigations. He teaches an iPhone forensics workshop in his spare time to train forensic examiners and corporate security personnel.

Jonathan is also a full-time research scientist specializing in machine learning technology to combat online fraud and spam, an effort that led him to develop networking products capable of learning how to protect customers. He is founder of the DSPAM project, a high-profile, next-generation spam filter that was acquired in 2006 by Sensory Networks, Inc. He lectures widely on the topic of spam and is a foremost researcher in the fields of machine-learning and algorithmic theory.