Build a Botnet Detection System for IoT Networks

IoT devices often have weak security and are targeted by attackers to form botnets, which can be used for DDoS attacks, data theft, or lateral movement. Detecting these infections early helps prevent device abuse and broader network compromise.

The system captures device activity and network traffic, extracts behavioral patterns, and uses rule-based heuristics or machine learning to classify whether a device is exhibiting botnet-like behavior — such as contacting known C2 servers or sending suspicious outbound traffic.

The system listens to IoT device traffic, particularly outbound packets. By analyzing frequency, destination, and timing patterns, it determines if the behavior matches known botnet profiles. Detected threats are logged, visualized, and optionally quarantined or blocked.

• Monitor traffic using a router tap, mirror port, or software sniffer.
• Extract traffic features like destination IP, port, packet count, and protocol usage.
• Match behavior against botnet signatures or apply ML-based classification.
• Log detected anomalies and alert the security team or system admin.
• Optional: auto-quarantine infected devices or block traffic temporarily.

• Zeek Network Security Monitor
• CICIDS2017 IoT Botnet Dataset
• Botnet Detection with ML
• Mirai Botnet Technical Analysis
• Passive Monitoring with Wireshark