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September 10, 2025Open Access

Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks

AWAbdullah WaqasNational University of Technology SKSultan Daud KhanNational University of Technology ZUZaib UllahGiustino Fortunato University

Key Points

CNN consistently outperforms LSTM and biLSTM for intrusion detection in balanced datasets, indicating its robustness.
Performance assessment across four benchmark datasets reveals LSTM and biLSTM's strength in temporal modeling under varying conditions.
Evaluating models under both balanced and imbalanced datasets underscores the need for adaptability in intrusion detection.
Findings point to the critical importance of selecting appropriate loss functions for optimizing model performance in IoT environments.

Abstract

The Internet of Things (IoT) holds transformative potential in fields such as power grid optimization, defense networks, and healthcare. However, the constrained processing capacities and resource limitations of IoT networks make them especially susceptible to cyber threats. This study addresses the problem of detecting intrusions in IoT environments by evaluating the performance of deep learning (DL) models under different data and algorithmic conditions. We conducted a comparative analysis of three widely used DL models—Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Bidirectional LSTM (biLSTM)—across four benchmark IoT intrusion detection datasets: BoTIoT, CiCIoT, ToNIoT, and WUSTL-IIoT-2021. Each model was assessed under balanced and imbalanced dataset configurations and evaluated using three loss functions (cross-entropy, focal loss, and dual focal loss). By analyzing model efficacy across these datasets, we highlight the importance of generalizability and adaptability to varied data characteristics that are essential for real-world applications. The results demonstrate that the CNN trained using the cross-entropy loss function consistently outperforms the other models, particularly on balanced datasets. On the other hand, LSTM and biLSTM show strong potential in temporal modeling, but their performance is highly dependent on the characteristics of the dataset. By analyzing the performance of multiple DL models under diverse datasets, this research provides actionable insights for developing secure, interpretable IoT systems that can meet the challenges of designing a secure IoT system.

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