Deep Learning Approach for IoT Traffic Multi-Classification in a Smart-City Scenario

Abstract

The recent explosive growth of Internet of Things (IoT) devices in smart cities has grown the attack surface of modern networks exponentially, calling for efficient and scalable intrusion detection means. Classical rule-based and classical machine learning (ML) approaches are typically not able to handle the heterogeneity and dynamic nature of IoT traffic. We recommend Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks combined in a hybrid deep learning (DL) model to effectively capture spatial and temporal dependencies in network flow data in this work. We preprocess ACI-IoT-2023 dataset with over 1.23 million records of benign and malware traffic through feature encoding, Min-Max normalization, and feature selection in order to present the inputs as balanced and optimized. Experimental results confirm that the suggested CNN-LSTM model performs superior with improved classification accuracy of 99.99% on average and near-perfect precision, recall, and F1-measures for every attack type. Comparison to traditional baselines including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), CNN, and LSTM indicates the robustness and durability of the proposed method. These results indicate that hybrid CNN-LSTM is a strong contender for real-time IoT intrusion detection in the context of smart cities.