Investigate the Use of Deep Learning in IoT Attack Detection

Abstract

The Internet of Things (IoT) has provided many benefits to society and introduced new security challenges. Attackers can target IoT devices to steal sensitive information or launch large-scale attacks. In this field, deep learning algorithms have provided encouraging results in the discovery and classification of intrusions in IoT devices. This study investigates the implementation and performance of four deep learning models: One-Dimensional Convolutional Neural Network (1DCNN), Long Short-Term Memory (LSTM), a hybrid 1DCNN-LSTM, and Two- Dimensional Convolutional Neural Network (2DCNN) for detecting and classifying IoT device attacks. Using the BoTNeTIoT-L01- v2 dataset, which includes normal and attack traffic provided by various IoT devices, we preprocess the data, extract features, and train the models, including weighted versions to optimize feature importance. Our findings highlight that the 2DCNN and hybrid 1DCNN-LSTM models shows superior performance, achieving high classification accuracy. This study contributes a comprehensive comparative analysis of deep learning models for IoT security, focusing on the effectiveness of weighted features in improving detection accuracy. The results provide valuable information for the advancement of real-time IoT attack detection systems.

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