This paper presents a novel intrusion detection and prevention framework for IoT networks using NS3 simulation, Random Forest classification, and Mario game behavioral mapping. The proposed system dynamically generates IoT attack datasets and analyzes multiple attack scenarios including DDoS, ransomware, eavesdropping, tracking, cyber espionage, and data mining attacks. The model combines machine learning classification with attack prevention mechanisms to improve network security and attack interpretation.
Chintha et al. (Wed,) studied this question.