The Experts below are selected from a list of 66600 Experts worldwide ranked by ideXlab platform
Sujeet Shenoi - One of the best experts on this subject based on the ideXlab platform.
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IFIP Int. Conf. Digital Forensics - Retrofitting Mobile Devices for Capturing Memory-Resident Malware Based on System Side-Effects
Advances in Digital Forensics XV, 2019Co-Authors: Zachary Grimmett, Jason Staggs, Sujeet ShenoiAbstract:Sophisticated memory-resident malware that target mobile phone platforms can be extremely difficult to detect and capture. However, triggering volatile memory captures based on observable system side-effects exhibited by malware can harvest live memory that contains memory-resident malware. This chapter describes a novel approach for capturing memory-resident malware on an Android Device for future analysis. The approach is demonstrated by making modifications to the Android debuggerd daemon to capture memory while a vulnerable process is being exploited on a Google Nexus 5 phone. The implementation employs an external Hardware Device to store a memory capture after successful exfiltration from the compromised mobile Device.
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Retrofitting Mobile Devices for Capturing Memory-Resident Malware Based on System Side-Effects
2019Co-Authors: Zachary Grimmett, Jason Staggs, Sujeet ShenoiAbstract:Sophisticated memory-resident malware that target mobile phone platforms can be extremely difficult to detect and capture. However, triggering volatile memory captures based on observable system side-effects exhibited by malware can harvest live memory that contains memory-resident malware. This chapter describes a novel approach for capturing memory-resident malware on an Android Device for future analysis. The approach is demonstrated by making modifications to the Android debuggerd daemon to capture memory while a vulnerable process is being exploited on a Google Nexus 5 phone. The implementation employs an external Hardware Device to store a memory capture after successful exfiltration from the compromised mobile Device.
Zachary Grimmett - One of the best experts on this subject based on the ideXlab platform.
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IFIP Int. Conf. Digital Forensics - Retrofitting Mobile Devices for Capturing Memory-Resident Malware Based on System Side-Effects
Advances in Digital Forensics XV, 2019Co-Authors: Zachary Grimmett, Jason Staggs, Sujeet ShenoiAbstract:Sophisticated memory-resident malware that target mobile phone platforms can be extremely difficult to detect and capture. However, triggering volatile memory captures based on observable system side-effects exhibited by malware can harvest live memory that contains memory-resident malware. This chapter describes a novel approach for capturing memory-resident malware on an Android Device for future analysis. The approach is demonstrated by making modifications to the Android debuggerd daemon to capture memory while a vulnerable process is being exploited on a Google Nexus 5 phone. The implementation employs an external Hardware Device to store a memory capture after successful exfiltration from the compromised mobile Device.
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Retrofitting Mobile Devices for Capturing Memory-Resident Malware Based on System Side-Effects
2019Co-Authors: Zachary Grimmett, Jason Staggs, Sujeet ShenoiAbstract:Sophisticated memory-resident malware that target mobile phone platforms can be extremely difficult to detect and capture. However, triggering volatile memory captures based on observable system side-effects exhibited by malware can harvest live memory that contains memory-resident malware. This chapter describes a novel approach for capturing memory-resident malware on an Android Device for future analysis. The approach is demonstrated by making modifications to the Android debuggerd daemon to capture memory while a vulnerable process is being exploited on a Google Nexus 5 phone. The implementation employs an external Hardware Device to store a memory capture after successful exfiltration from the compromised mobile Device.
Jason Staggs - One of the best experts on this subject based on the ideXlab platform.
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IFIP Int. Conf. Digital Forensics - Retrofitting Mobile Devices for Capturing Memory-Resident Malware Based on System Side-Effects
Advances in Digital Forensics XV, 2019Co-Authors: Zachary Grimmett, Jason Staggs, Sujeet ShenoiAbstract:Sophisticated memory-resident malware that target mobile phone platforms can be extremely difficult to detect and capture. However, triggering volatile memory captures based on observable system side-effects exhibited by malware can harvest live memory that contains memory-resident malware. This chapter describes a novel approach for capturing memory-resident malware on an Android Device for future analysis. The approach is demonstrated by making modifications to the Android debuggerd daemon to capture memory while a vulnerable process is being exploited on a Google Nexus 5 phone. The implementation employs an external Hardware Device to store a memory capture after successful exfiltration from the compromised mobile Device.
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Retrofitting Mobile Devices for Capturing Memory-Resident Malware Based on System Side-Effects
2019Co-Authors: Zachary Grimmett, Jason Staggs, Sujeet ShenoiAbstract:Sophisticated memory-resident malware that target mobile phone platforms can be extremely difficult to detect and capture. However, triggering volatile memory captures based on observable system side-effects exhibited by malware can harvest live memory that contains memory-resident malware. This chapter describes a novel approach for capturing memory-resident malware on an Android Device for future analysis. The approach is demonstrated by making modifications to the Android debuggerd daemon to capture memory while a vulnerable process is being exploited on a Google Nexus 5 phone. The implementation employs an external Hardware Device to store a memory capture after successful exfiltration from the compromised mobile Device.
Ken Morley - One of the best experts on this subject based on the ideXlab platform.
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method and system for performing speech recognition for an internet appliance using a remotely located speech recognition application
2000Co-Authors: Todor Cooklev, Darrin Gibbs, Mark Gray, Ken MorleyAbstract:A method and system for performing speech recognition for an internet appliance using a remotely located speech recognition application. The invention includes an internet appliance that is connected through a network with either a stand-alone computer or a server computer located at the Internet Service Provider. Verbal commands directed to an internet appliance are received as analog signals and converted to digital signals. The digital signals are remotely translated into a set of instructions by a dedicated Hardware Device or a software program that operates a speech recognition application at either a stand-alone computer or server computer located at an Internet Service Provider in a form recognizable by the internet appliance. The internet appliance receives and executes the translated instructions.
Nikita Chauhan - One of the best experts on this subject based on the ideXlab platform.
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a virtual instrument oscilloscope for signal measurements
2015Co-Authors: Nikita ChauhanAbstract:In this paper, we discuss about the LabVIEW based graphical applications for the measurements of signal. A virtual instrument is designed in order to serve the purpose of oscilloscope with a user friendly interface at the front panel. The designed oscilloscope is derived from a Hardware instrument named Digital Phosphor Oscilloscope TDS 5104B.Firstly the original Hardware Device functions are examined, then a labview based VI is designed holding all the similar features like TDS 5104B.Front panel is user defined with user specific controls resulting in easy, compact and cost effective system. It is proved that the virtual instrument results in a powerful, productive, efficient, power saving and a precise measuring instrument. It provides wide range of application in future and proves to be a practical yet manipulative Device.
