A Highly-Efficient Memory-Compression Scheme for GPU-Accelerated Intrusion Detection Systems
This work addresses the need for efficient intrusion detection in high-volume data environments, though it appears incremental as it builds on existing Aho-Corasick methods with GPU optimizations.
The paper tackles the computational challenge of high-speed pattern matching for intrusion detection systems by presenting a highly compressed failure-less Aho-Corasick algorithm optimized for GPUs, achieving speeds up to 8 Gbps with low memory consumption.
Pattern Matching is a computationally intensive task used in many research fields and real world applications. Due to the ever-growing volume of data to be processed, and increasing link speeds, the number of patterns to be matched has risen significantly. In this paper we explore the parallel capabilities of modern General Purpose Graphics Processing Units (GPGPU) applications for high speed pattern matching. A highly compressed failure-less Aho-Corasick algorithm is presented for Intrusion Detection Systems on off-the-shelf hardware. This approach maximises the bandwidth for data transfers between the host and the Graphics Processing Unit (GPU). Experiments are performed on multiple alphabet sizes, demonstrating the capabilities of the library to be used in different research fields, while sustaining an adequate throughput for intrusion detection systems or DNA sequencing. The work also explores the performance impact of adequate prefix matching for alphabet sizes and varying pattern numbers achieving speeds up to 8Gbps and low memory consumption for intrusion detection systems.