Real-Time AI-Powered DDoS Detection (www.timeplus.com)

Timeplus demonstrated a real-time DDoS detection pipeline that integrates its streaming SQL engine with OpenAI’s GPT-3.5 to augment rule-based network analytics with LLM-powered anomaly detection and reasoning. The system ingests IP flow data (e.g., via Kafka), runs streaming SQL in Timeplus, and invokes a remote Python UDF that formats a few-shot prompt and calls the OpenAI API. When the LLM flags an event as DDoS, Timeplus triggers alerts and downstream rule engines to block malicious IPs. This hybrid approach is significant because it combines low-latency stream processing with LLM flexibility, enabling detection of attack patterns that are hard to encode as static rules while producing human-readable reasoning for triage. Technically, the UDF (is_ddos) uses features from the CIC-IDS2017 dataset—Bwd Packet Length Min/Std, Average Packet Size, Flow Duration, and Flow IAT Std—by sampling 10 labeled examples (interleaved five benign/five DDoS) as context and scoring a single test flow via GPT-3.5; the referenced study reported ~90% detection accuracy and GPT-3.5 outperformed a neural net baseline. Implementation details include prompt construction, regex extraction of the predicted label, Dockerized deployment, and a conservative event rate (eps=0.1) to account for API latency. Key implications: LLMs add explainability and adaptability to real-time security stacks, but introduce trade-offs around latency, API cost, privacy, and robustness to adversarial or drifting traffic—operational safeguards and continual evaluation will be necessary.