An efficient cyber-attack detection and classification in IoT networks with high-dimensional feature set using Levenberg-Marquardt optimized feedforward neural network

Abstract

This paper examines the escalating challenge of detecting cyber-attacks within Internet of Things (IoT) networks, where conventional security measures often falter in addressing the speed and complexity of contemporary threats. In response to the necessity for more precise, efficient, and adaptive security solutions, we propose a deep learning-based approach that employs feedforward neural networks optimized through the Levenberg-Marquardt algorithm. Our findings indicate that this method markedly surpasses traditional machine learning and deep learning models, such as Support Vector Machines (SVM), Random Forest, and Artificial Neural Network (ANN), achieving an accuracy rate of 99.7%, precision of 99.93%, recall of 99.93%, and an F1-score of 99.93%. Furthermore, the model demonstrates minimal misclassifications and effectively processes substantial data volumes, rendering it highly suitable for the real-time detection of various cyber threats. This system substantially reduces false positive rates and enhances the classification accuracy of different attack types within IoT networks. This research contributes to the advancement of cybersecurity in IoT environments by providing a scalable and robust solution for identifying emerging cyber threats.