A Deep Learning-Based Framework for Cyber Attack Detection in IoT Networks

Abstract

The rapid growth of the Internet of Things (IoT) has connected billions of smart devices, making daily life more convenient while also increasing the risk of cyber threats. Traditional security methods often struggle to identify complex and evolving attacks in largescale IoT environments. This project presents an intelligent cyberattack detection system that applies machine learning and deep learning techniques to classify network traffic as either normal or malicious. The collected IoT dataset is first pre-processed by handling missing values, converting categorical features into numerical form, and normalizing the data. Principal Component Analysis (PCA) is then used to reduce the number of features while preserving the most important information. Two classification models, namely a Multi-Layer Perceptron (MLP) neural network and a Random Forest classifier, are trained and evaluated using the processed dataset. Their performance is compared based on prediction accuracy and classification results. Experimental findings show that the neural network achieves superior accuracy and effectively detects various attack categories with minimal false predictions. The developed system offers a reliable, scalable, and efficient solution for securing IoT networks against cyber-attacks. This approach demonstrates the potential of machine learningbased intrusion detection systems in improving network security and supporting the protection of modern smart environments.