NDLI: GLoP: Enabling Massively Parallel Incident Response Through GPU Log Processing

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GLoP: Enabling Massively Parallel Incident Response Through GPU Log Processing

Content Provider	ACM Digital Library
Author	Atkinson, Robert C. Bellekens, Xavier J. A. Tachtatzis, Christos Renfrew, Craig Kirkham, Tony
Abstract	Large industrial systems that combine services and applications, have become targets for cyber criminals and are challenging from the security, monitoring and auditing perspectives. Security log analysis is a key step for uncovering anomalies, detecting intrusion, and enabling incident response. The constant increase of link speeds, threats and users, produce large volumes of log data and become increasingly difficult to analyse on a Central Processing Unit (CPU). This paper presents a massively parallel Graphics Processing Unit (GPU) Log Processing (GLoP) library and can also be used for Deep Packet Inspection (DPI), using a prefix matching technique, harvesting the full power of off-the-shelf technologies. GLoP implements two different algorithm using different GPU memory and is compared against CPU counterpart implementations. The library can be used for processing nodes with single or multiple GPUs as well as GPU cloud farms. The results show throughput of 20 Gbps and demonstrate that modern GPUs can be utilised to increase the operational speed of large scale log processing scenarios, saving precious time before and after an intrusion has occurred.
Starting Page	295
Ending Page	301
Page Count	7
File Format	PDF
ISBN	9781450330336
DOI	10.1145/2659651.2659700
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Security Gpu Pattern matching Cuda
Content Type	Text
Resource Type	Article

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