Gnort: High Performance Network Intrusion Detection Using Graphics Processors
RAID '08 Proceedings of the 11th international symposium on Recent Advances in Intrusion Detection
Fast packet classification for Snort by native compilation of rules
LISA'08 Proceedings of the 22nd conference on Large installation system administration conference
A hardware platform for efficient worm outbreak detection
ACM Transactions on Design Automation of Electronic Systems (TODAES)
Regular Expression Matching on Graphics Hardware for Intrusion Detection
RAID '09 Proceedings of the 12th International Symposium on Recent Advances in Intrusion Detection
Experiences with string matching on the fermi architecture
ARCS'11 Proceedings of the 24th international conference on Architecture of computing systems
Massive threading: Using GPUs to increase the performance of digital forensics tools
Digital Investigation: The International Journal of Digital Forensics & Incident Response
Deep packet inspection tools and techniques in commodity platforms: Challenges and trends
Journal of Network and Computer Applications
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Signature-matching Intrusion Detection Systems can experience significant decreases in performance when the load on the IDS-host increases. We propose a solution that off-loads some of the computation performed by the IDS to the Graphics Processing Unit (GPU). Modern GPUs are programmable, stream-processors capable of high-performance computing that in recent years have been used in non-graphical computing tasks. The major operation in a signature-matching IDS is matching values seen operation to known black-listed values, as such, our solution implements the string-matching on the GPU. The results show that as the CPU load on the IDS host system increases, PixelSnort's performance is significantly more robust and is able to outperform conventional Snort by up to 40%.