What question did this study set out to answer?

This research aims to develop a framework that enhances intrusion detection and prevention mechanisms in IoT networks.

May 15, 2026Open Access

NS3-Based Mario Game Approach for Intrusion Detection and Prevention in IoT Networks

Puntos clave

This research aims to develop a framework that enhances intrusion detection and prevention mechanisms in IoT networks.
Utilizes NS3 simulation for IoT attack dataset generation.
Implements Random Forest classification for machine learning.
Analyzes multiple attack scenarios including DDoS, ransomware, and cyber espionage.
Successfully identifies various attack types with high accuracy using machine learning models.
Demonstrates improved network security measures through dynamic attack mapping.
Provides effective interpretation of attacks for better prevention strategies.

Resumen

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.

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