Sand Cat Swarm Intelligent based Random Forest Approach for DDoS Attack Detection in IoT Network Scenario using NS3
Shilpa Shashikant Chaudhari, T N Sparshika, D Preethi, Chandana S Muttur, Anushree Maligehalli Shadaksharaiah · 2024
The last decade have seen a notable expansion and advancement of the Internet of Things (IoT), providing novel solutions to industrial and social problems. Launching DDoS attack poses a great threat to IoT network and is hard to detect due to their stealth and seemingly legitimate nature. While progress has been made to address DDoS, further work has to be done beyond the scope of current study to fully integrate Multiview features and capture rich semantic relationships. The Random Forest classifier is a potent supplement to more conventional machine learning-based categorization techniques when used on network traffic data. This paper suggests a approach that makes use of sand cat swarm intelligent for feature optimization and random forest classifier for the quick and accurate identification of risks in Internet of Things networks. The suggested approach reduces training time and improves DDoS detection effectiveness, suggesting an improved method for enhancing IoT network security.