The increasing sophistication of multi-faceted attacks (MFAs) presents significant challenges for securing Internet of Things (IoT) networks, where traditional defenses and even contemporary solutions often fail to provide comprehensive protection. Current frameworks in the literature face critical limitations such as centralized control architectures that are prone to bottlenecks and single points of failure, inadequate traffic monitoring capabilities, and limited adaptability to dynamic attack surfaces. These gaps make IoT environments vulnerable to stealthy, coordinated, and complex attacks that can simultaneously target multiple layers of the network. Addressing these challenges requires a more dynamic and distributed approach to security. This paper introduces CO-STOP, an innovative framework designed to overcome these limitations by integrating machine learning (ML), the P4 programming language, Software …