High-Throughput Machine Learning Approaches for Network Attacks Detection on FPGA

The popularity of applying Artificial Intelligence (AI) to perform prediction and automation tasks has become one of the most conspicuous trends in computer science. However, AI systems usually require heavy computational tasks and result in violating applications that need real-time interactions. In this work, we propose a system which is a combination of FPGA platform and AI to achieve a high-throughput network attacks detection. Our architecture consists of 2 well-known and powerful classification techniques, which are the Decision Tree and Neural Network. To prove the feasibility of the proposed approach, we implement a prototype on NetFPGA-10G board using Verilog-HDL. Moreover, the prototype is trained and tested with NSL-KDD dataset, the most popular dataset for network attack detection system. Our experimental results show that the Neural network core can detect attacks with speed at up to 9.86 Gbps for all packet sizes from 64B to 1500B, which is thoroughly 11x and 83x times faster than Geforce GTX 850M GPU and i5 8th generation CPU, respectively. The Neural Network classifier system can function at 104.091 MHz and achieve the accuracy at 87.3.