Building an Automated Pipeline in the End

[IV-cmd]

Objective
Creating an automated pipeline in the end that detects suspicious traffic with low-interaction honeypots, triggers alerts, auto-spawns high-interaction honeypots for deep analysis, and provides feedback to system to improve efficiency and reduce costs. This reduces the cloud bill to a greater extent

Pipeline Architecture

                                               Internet Traffic
                                                           ↓
Low-Interaction Honeypots --- (Cheap, Scalable detections)[SSH, HTTP decoys]
                                                           ↓
     Event Engine (Kafka, Redis) --- (Real time Analysis and Attack Classification)
                                                           ↓
Auto-Spawn High Interaction Honeypots --- (On-demand deep analysis, Full OS env, Isolated Safe Zone)
                                                           ↓
   Feedback Loop ML Engine Efficiency --- (System Optimization, Cost Reduction, Pattern Learning)

Benefits
-Cost Efficiency

• Low-HP: Always running (cheap)
• High-HP: Only when needed (expensive)

-Smart Detection

• Low-HP: Catch 95% of attacks
• Auto-spawn: Deep analysis for interesting and complex attacks

-Automated Response

• Detection → Trigger → Analysis: <30 seconds
• No manual intervention required
• 24/7 automated defense system