  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.
