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Golden G. Richard III, ""Memory Analysis, Meet GPU Malware""

 
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When? This feed was archived on January 12, 2017 15:24 (7y ago). Last successful fetch was on September 14, 2016 19:32 (7+ y ago)

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Graphics Processing Units (GPUs) have evolved from very specialized, idiosyncratic hardware intended to execute specialized graphics workloads to semi-autonomous "supercomputers" that can be programmed easily using common programming languages and powerful, portable APIs. GPUs also form the basis for an emerging threat, GPU malware, which offloads important aspects of malicious computations onto the GPU. The benefits of executing malicious computations on the GPU include abundant compute power, a large amount of semi-non-volatile memory, and perhaps most importantly, isolation from host-based security measures. While memory analysis offers powerful tools to detect and analyze traditional host-based malware, there are essentially no equivalent tools for analyzing GPU malware. Furthermore, existing general-purpose tools for debugging GPU applications are completely ineffective if a large number of conditions are not established before a GPU application is executed, all of which will certainly be violated by weaponized GPU malware. This talk explores GPU malware in detail, identifies why it's hard to analyze, and also discusses measures that can easily employed to make analysis even more difficult. A primary motivation for this research is the 2015 DFRWS Digital Forensics Challenge, under development by Dr. Richard, the aim of which is to increase interest in GPU malware analysis and foster the development of powerful tools to analyze and combat this threat.
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322 episodes

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Archived series ("Inactive feed" status)

When? This feed was archived on January 12, 2017 15:24 (7y ago). Last successful fetch was on September 14, 2016 19:32 (7+ y ago)

Why? Inactive feed status. Our servers were unable to retrieve a valid podcast feed for a sustained period.

What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.

Manage episode 53053269 series 39330
Content provided by CERIAS. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by CERIAS or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
Graphics Processing Units (GPUs) have evolved from very specialized, idiosyncratic hardware intended to execute specialized graphics workloads to semi-autonomous "supercomputers" that can be programmed easily using common programming languages and powerful, portable APIs. GPUs also form the basis for an emerging threat, GPU malware, which offloads important aspects of malicious computations onto the GPU. The benefits of executing malicious computations on the GPU include abundant compute power, a large amount of semi-non-volatile memory, and perhaps most importantly, isolation from host-based security measures. While memory analysis offers powerful tools to detect and analyze traditional host-based malware, there are essentially no equivalent tools for analyzing GPU malware. Furthermore, existing general-purpose tools for debugging GPU applications are completely ineffective if a large number of conditions are not established before a GPU application is executed, all of which will certainly be violated by weaponized GPU malware. This talk explores GPU malware in detail, identifies why it's hard to analyze, and also discusses measures that can easily employed to make analysis even more difficult. A primary motivation for this research is the 2015 DFRWS Digital Forensics Challenge, under development by Dr. Richard, the aim of which is to increase interest in GPU malware analysis and foster the development of powerful tools to analyze and combat this threat.
  continue reading

322 episodes

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