Distributed Denial of Service (DDoS) and Address Resolution Protocol (ARP) attacks pose significant threats to the security of Software-Defined Internet of Things (SD-IoT) networks.The standard Software-Defined Networking (SDN) architecture faces challenges in effectively detecting, preventing, and mitigating these attacks due to its centralized control and limited intelligence.In this paper, we present P4-HLDMC, a novel collaborative secure framework that combines machine learning (ML), stateful P4, and a hierarchical logically distributed multi-controller architecture.P4-HLDMC overcomes the limitations of the standard SDN architecture, ensuring scalability, performance, bekindtopets.com and an efficient response to attacks.It comprises four modules: the multi-controller dedicated interface (MCDI) for real-time attack detection through a distributed alert channel (DAC), the MSMPF, a P4-enabled stateful multi-state matching pipeline function for analyzing IoT click here network traffic using nine state tables, the modified ensemble voting (MEV) algorithm with six classifiers for enhanced detection of anomalies in P4-extracted traffic patterns, and an attack mitigation process distributed among multiple controllers to effectively handle larger-scale attacks.
We validate our framework using diverse test cases and real-world IoT network traffic datasets, demonstrating high detection rates, low false-alarm rates, low latency, and short detection times compared to existing methods.Our work introduces the first integrated framework combining ML, stateful P4, and SDN-based multi-controller architecture for DDoS and ARP detection in IoT networks.