Sophisticated traffic analytics, such as the encrypted traffic analytics and
unknown malware detection, emphasizes the need for advanced methods to analyze
the network traffic. Traditional methods of using fixed patterns, signature
matching, and rules to detect known patterns in network traffic are being
replaced with AI (Artificial Intelligence) driven algorithms. However, the
absence of a high-performance AI networking-specific framework makes deploying
real-time AI-based processing within networking workloads impossible. In this
paper, we describe the design of Traffic Analytics Development Kits (TADK), an
industry-standard framework specific for AI-based networking workloads
processing. TADK can provide real-time AI-based networking workload processing
in networking equipment from the data center out to the edge without the need
for specialized hardware (e.g., GPUs, Neural Processing Unit, and so on). We
have deployed TADK in commodity WAF and 5G UPF, and the evaluation result shows
that TADK can achieve a throughput up to 35.3Gbps per core on traffic feature
extraction, 6.5Gbps per core on traffic classification, and can decrease
SQLi/XSS detection down to 4.5us per request with higher accuracy than fixed
pattern solution.

本研究介绍了 Traffic Analytics Development Kits (TADK) 的设计，是一个行业标准的、针对基于 AI 的网络工作负载处理的框架，可以在网络设备中提供实时的 AI 网络工作负载处理，无需专门的硬件。通过部署 TADK 在 WAF 和 5G UPF 以上，评估结果显示，在流量特征提取方面，可以实现每个核心高达 35.3Gbps 的吞吐量，流量分类方面可以实现每个核心 6.5Gbps 的吞吐量，且可以将 SQLi/XSS 检测时间降低到每个请求 4.5us，并具有比固定模式解决方案更高的准确性。