Benchmarking Cyber Deception: Why Legacy Honeypots Fail Under High Concurrency (And How EchidraOSS Fixes It)

说明与用途

传统蜜罐在高并发下表现不佳，导致 CPU 峰值和内存膨胀。EchidraOSS 采用异步 FastAPI 事件循环和确定性分类，能够高效处理突发流量，CPU 低于 20% ，内存约 90 MB，并提供实时 MITRE ATT&CK 映射。

问题

Legacy honeypots struggle with high concurrency, leading to dropped connections, high CPU usage, and memory bloat during burst traffic from botnets and scanners.

结果

EchidraOSS handles high-concurrency bursts with <20 % CPU usage and ~90 MB RAM, compared to legacy tools that spike near 100 % CPU and 180 MB RAM. It provides real-time, deterministic threat classification without post-processing.

工作流

• 1. Deploy EchidraOSS via Docker Compose on a 1 vCPU, 1 GB VPS.
• 2. Configure listeners for desired protocols (SSH, HTTP, FTP, Telnet).
• 3. Run load tests or observe real traffic; the async event loop handles bursts without CPU spikes.
• 4. Sessions are classified in‑process at close, assigning actor, intent, risk score, and MITRE IDs.
• 5. View grouped issues in the Intelligence dashboard with evidence, recommended fixes, and impact summaries.

证据

CPU utilization over time under a simulated connection flood, thread-per-connection versus async event loop.

来源

RAM usage of the full stack, legacy Twisted/threaded honeypots versus EchidraOSS's FastAPI async stack.

来源

EchidraOSS classifies sessions natively, at capture time, using a deterministic rule engine.

来源