Investigate the Use of Deep Learning in IoT Attack Detection
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 cla...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Croatian Communications and Information Society (CCIS)
2025-06-01
|
| Series: | Journal of Communications Software and Systems |
| Subjects: | |
| Online Access: | https://jcoms.fesb.unist.hr/10.24138/jcomss-2024-0101/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850142455720050688 |
|---|---|
| author | Mohamed Saddek Ghozlane Adlen Kerboua Smaine Mazouzi Lakhdar Laimeche |
| author_facet | Mohamed Saddek Ghozlane Adlen Kerboua Smaine Mazouzi Lakhdar Laimeche |
| author_sort | Mohamed Saddek Ghozlane |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ff8bebd96434421aa0accac2dee672a6 |
| institution | OA Journals |
| issn | 1845-6421 1846-6079 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Croatian Communications and Information Society (CCIS) |
| record_format | Article |
| series | Journal of Communications Software and Systems |
| spelling | doaj-art-ff8bebd96434421aa0accac2dee672a62025-08-20T02:29:04ZengCroatian Communications and Information Society (CCIS)Journal of Communications Software and Systems1845-64211846-60792025-06-0121222923910.24138/jcomss-2024-0101Investigate the Use of Deep Learning in IoT Attack DetectionMohamed Saddek GhozlaneAdlen KerbouaSmaine MazouziLakhdar LaimecheThe 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.https://jcoms.fesb.unist.hr/10.24138/jcomss-2024-0101/deep learningiotattackbotnet |
| spellingShingle | Mohamed Saddek Ghozlane Adlen Kerboua Smaine Mazouzi Lakhdar Laimeche Investigate the Use of Deep Learning in IoT Attack Detection Journal of Communications Software and Systems deep learning iot attack botnet |
| title | Investigate the Use of Deep Learning in IoT Attack Detection |
| title_full | Investigate the Use of Deep Learning in IoT Attack Detection |
| title_fullStr | Investigate the Use of Deep Learning in IoT Attack Detection |
| title_full_unstemmed | Investigate the Use of Deep Learning in IoT Attack Detection |
| title_short | Investigate the Use of Deep Learning in IoT Attack Detection |
| title_sort | investigate the use of deep learning in iot attack detection |
| topic | deep learning iot attack botnet |
| url | https://jcoms.fesb.unist.hr/10.24138/jcomss-2024-0101/ |
| work_keys_str_mv | AT mohamedsaddekghozlane investigatetheuseofdeeplearninginiotattackdetection AT adlenkerboua investigatetheuseofdeeplearninginiotattackdetection AT smainemazouzi investigatetheuseofdeeplearninginiotattackdetection AT lakhdarlaimeche investigatetheuseofdeeplearninginiotattackdetection |