Comparative Analysis of State-of-the-Art Attack Detection Models
Priyanka Kumari, Veenu Mangat, Anshul Kumar Singh · 2023
Based on the current situation, IoT devices are being used a lot and the more its use increases, the more our personal information security will weaker. Many attacks are available to break these security systems, but the saying goes that if one door closes, ten doors are open. We have many techniques available to detect and avoid attacks that is machine learning, deep learning etc. This article unveils a comparative analysis of state-of-the-art machine learning classifiers on two different datasets regarding identifying intrusion in network traffic.The research question is which machine learning algorithm performs the best for detecting different types of malicious activity in IoT network traffic. We evaluated the performance metrics which comprise accuracy, precision, and recall. The proposed work can achieve outstanding performance compared to the existing approaches.The classification has achieved 99.11% of accuracy for the IoT Network intrusion dataset and 99.99% accuracy for the IoT_23 dataset