Abstract
Deep Packet Inspection (DPI) is necessitated for many networked application systems in order to prevent from cyber threats. The signature based Network Intrusion and etection System (NIDS) works on packet inspection and pattern matching mechanisms for the detection of malicious content in network traffic. The rapid growth of high speed networks in data centers demand an efficient high speed packet processing mechanism which is also capable of malicious packets detection. In this paper, we proposed a framework GDPI for efficient packet processing which inspects all incoming packet’s payload with known signature patterns, commonly available is Snort. The framework is developed using enhanced GPU programming techniques, such as asynchronous packet processing using streams, minimizing CPU to GPU latency using pinned memory and zero copy, and memory coalescing with shared memory which reduces read operation from global memory of the GPU. The overall performance of GDPI is tested on heterogeneous NVIDIA GPUs, like Tegra Tk1, GTX 780, and Tesla K40 and observed that the highest throughput is achieved with Tesla K40. The design code of GDPI is made available for research community.
Highlights
Deep Packet Inspection (DPI) is a challenging task which involves network packet filtering mechanism
The framework is designed considering modular programming approach in a way that either of pattern matching algorithms,KMP or Rabin Karp can be selected for the patterns to be matched.The modular approach facilitates the integration of different pattern matching algorithms as per need.The memory coalescing technique is used for patterns residing in Graphic Processing Units (GPUs) shared memory to be matched with incoming packets, which reduces the read operations and increases the overall packet processing speed
The rest of this paper is organized as follows: Section II consists of related work which discusses the recent trends in high performance intrusion detection systems, regular expression matching methods for deep packet inspection, packet processing techniques on GPUs, pattern matching algorithms for DPI and machine learning methods used for DPI
Summary
Deep Packet Inspection (DPI) is a challenging task which involves network packet filtering mechanism. The research is motivated with above mentioned challenges and requirements The incoming packet’s payload is inspected using open source CUDA based pattern matching algorithms, KMP and Rabin Karp. The framework is designed considering modular programming approach in a way that either of pattern matching algorithms,KMP or Rabin Karp can be selected for the patterns to be matched.The modular approach facilitates the integration of different pattern matching algorithms as per need.The memory coalescing technique is used for patterns residing in GPU shared memory to be matched with incoming packets, which reduces the read operations and increases the overall packet processing speed. Used a modular programming approach considering the selection of either of pattern matching algorithm www.ijacsa.thesai.org
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