Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks
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...
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MDPI AG
2025-07-01
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| author | Abdullah Waqas Sultan Daud Khan Zaib Ullah Mohib Ullah Habib Ullah |
| author_facet | Abdullah Waqas Sultan Daud Khan Zaib Ullah Mohib Ullah Habib Ullah |
| author_sort | Abdullah Waqas |
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| description | 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. |
| format | Article |
| id | doaj-art-7d9a57a33a3d4d65806ff454991bcdc0 |
| institution | Matheson Library |
| issn | 2073-431X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
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| spelling | doaj-art-7d9a57a33a3d4d65806ff454991bcdc02025-07-25T13:19:03ZengMDPI AGComputers2073-431X2025-07-0114728310.3390/computers14070283Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT NetworksAbdullah Waqas0Sultan Daud Khan1Zaib Ullah2Mohib Ullah3Habib Ullah4Department of Computer Science, National University of Technology, Islamabad 44000, PakistanDepartment of Computer Science, National University of Technology, Islamabad 44000, PakistanDepartment of Computer Engineering, Universitá Telematica Giustino Fortunato, 82100 Benevento, ItalyIntelligent Systems and Analytics (ISA) Research Group, Department of Computer Science (IDI), Norwegian University of Science and Technology (NTNU), 2815 Gjøvik, NorwayFaculty of Science and Technology (REALTEK), Norwegian University of Life Sciences (NMBU), 1433 Ås, NorwayThe 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.https://www.mdpi.com/2073-431X/14/7/283IoTcybersecurityintrusion detectionToNIoTCiCIoTdeep learning |
| spellingShingle | Abdullah Waqas Sultan Daud Khan Zaib Ullah Mohib Ullah Habib Ullah Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks Computers IoT cybersecurity intrusion detection ToNIoT CiCIoT deep learning |
| title | Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks |
| title_full | Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks |
| title_fullStr | Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks |
| title_full_unstemmed | Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks |
| title_short | Comparative Analysis of Deep Learning Models for Intrusion Detection in IoT Networks |
| title_sort | comparative analysis of deep learning models for intrusion detection in iot networks |
| topic | IoT cybersecurity intrusion detection ToNIoT CiCIoT deep learning |
| url | https://www.mdpi.com/2073-431X/14/7/283 |
| work_keys_str_mv | AT abdullahwaqas comparativeanalysisofdeeplearningmodelsforintrusiondetectioniniotnetworks AT sultandaudkhan comparativeanalysisofdeeplearningmodelsforintrusiondetectioniniotnetworks AT zaibullah comparativeanalysisofdeeplearningmodelsforintrusiondetectioniniotnetworks AT mohibullah comparativeanalysisofdeeplearningmodelsforintrusiondetectioniniotnetworks AT habibullah comparativeanalysisofdeeplearningmodelsforintrusiondetectioniniotnetworks |