Data-Driven Detection of Multi-vector IoT DDoS Attacks across Network Layers
—As cyberattacks targeting Internet of Things (IoT) networks grow more sophisticated, the demand for models capable of accurately detecting and mitigating these threats becomes increasingly urgent existing detection systems often concentrate on a single attack surface which leads to critical blind spots in IoT network monitoring. This research introduces an intensive IoT attack-detection framework that addresses internal and external attack sources, leveraging multi-layer attack vectors across the application, transport, network, and data link layers. The proposed framework was evaluated in two phases. In the first phase, three datasets that simulate a distinct attack source were created: outbound, including Dynamic Host Configuration Protocol (DHCP) amplification and DHCP starvation; inbound attacks, including application-layer attacks, Synchronize (SYN) flood, User Datagram Protocol (UDP) flood, Internet Control Message Protocol (ICMP) Smurf, and ICMP direct; and internal Address Resolution Protocol (ARP) spoofing. Three machine learning models; Random Forest, Light Gradient-Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) were evaluated using accuracy, precision, recall, F1-Score, and Receiver Operating Characteristic (ROC) curve. In the second phase, a new dataset was generated by combining all datasets from the first phase. On the combined dataset, LightGBM achieved the highest performance, with accuracy: 94.08%, precision: 93.94%, recall: 94.08%, and F1-Score: 93.90%. Random Forest and CatBoost showed comparable performance, with all metrics ranging from approximately 93.2% to 93.9%. LightGBM demonstrates a slight edge in overall detection performance compared to other models, which highlights its effectiveness in detecting diverse and complex attack patterns across multiple traffic directions.