Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT
The IoT’s quick development has brought up several security problems and issues that cannot be solved using traditional intelligent systems. Deep learning (DL) in the field of artificial intelligence (AI) has proven to be efficient, with many advantages that can be used to address IoT cybersecurity...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/19/9572 |
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| author | Imad Tareq Bassant M. Elbagoury Salsabil El-Regaily El-Sayed M. El-Horbaty |
| author_facet | Imad Tareq Bassant M. Elbagoury Salsabil El-Regaily El-Sayed M. El-Horbaty |
| author_sort | Imad Tareq |
| collection | DOAJ |
| description | The IoT’s quick development has brought up several security problems and issues that cannot be solved using traditional intelligent systems. Deep learning (DL) in the field of artificial intelligence (AI) has proven to be efficient, with many advantages that can be used to address IoT cybersecurity concerns. This study trained two models of intelligent networks—namely, DenseNet and Inception Time—to detect cyber-attacks based on a multi-class classification method. We began our investigation by measuring the performance of these two networks using three datasets: the ToN-IoT dataset, which consists of heterogeneous data; the Edge-IIoT dataset; and the UNSW2015 dataset. Then, the results were compared by identifying several cyber-attacks. Extensive experiments were conducted on standard ToN-IoT datasets using the DenseNet multicategory classification model. The best result we obtained was an accuracy of 99.9% for Windows 10 with DenseNet, but by using the Inception Time approach we obtained the highest result for Windows 10 with the network, with 100% accuracy. As for using the Edge-IIoT dataset with the Inception Time approach, the best result was an accuracy of 94.94%. The attacks were also assessed in the UNSW-NB15 database using the Inception Time approach, which had an accuracy rate of 98.4%. Using window sequences for the sliding window approach and a six-window size to start training the Inception Time model yielded a slight improvement, with an accuracy rate of 98.6% in the multicategory classification. |
| first_indexed | 2024-03-09T22:05:40Z |
| format | Article |
| id | doaj.art-81aa1ed284554d89adfc6bcbe28b1488 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T22:05:40Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-81aa1ed284554d89adfc6bcbe28b14882023-11-23T19:41:54ZengMDPI AGApplied Sciences2076-34172022-09-011219957210.3390/app12199572Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoTImad Tareq0Bassant M. Elbagoury1Salsabil El-Regaily2El-Sayed M. El-Horbaty3Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, EgyptFaculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, EgyptFaculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, EgyptFaculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, EgyptThe IoT’s quick development has brought up several security problems and issues that cannot be solved using traditional intelligent systems. Deep learning (DL) in the field of artificial intelligence (AI) has proven to be efficient, with many advantages that can be used to address IoT cybersecurity concerns. This study trained two models of intelligent networks—namely, DenseNet and Inception Time—to detect cyber-attacks based on a multi-class classification method. We began our investigation by measuring the performance of these two networks using three datasets: the ToN-IoT dataset, which consists of heterogeneous data; the Edge-IIoT dataset; and the UNSW2015 dataset. Then, the results were compared by identifying several cyber-attacks. Extensive experiments were conducted on standard ToN-IoT datasets using the DenseNet multicategory classification model. The best result we obtained was an accuracy of 99.9% for Windows 10 with DenseNet, but by using the Inception Time approach we obtained the highest result for Windows 10 with the network, with 100% accuracy. As for using the Edge-IIoT dataset with the Inception Time approach, the best result was an accuracy of 94.94%. The attacks were also assessed in the UNSW-NB15 database using the Inception Time approach, which had an accuracy rate of 98.4%. Using window sequences for the sliding window approach and a six-window size to start training the Inception Time model yielded a slight improvement, with an accuracy rate of 98.6% in the multicategory classification.https://www.mdpi.com/2076-3417/12/19/9572DenseNetinception timecyber securitymalware detectionToN-IoT datasetUNSW2015 dataset |
| spellingShingle | Imad Tareq Bassant M. Elbagoury Salsabil El-Regaily El-Sayed M. El-Horbaty Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT Applied Sciences DenseNet inception time cyber security malware detection ToN-IoT dataset UNSW2015 dataset |
| title | Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT |
| title_full | Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT |
| title_fullStr | Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT |
| title_full_unstemmed | Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT |
| title_short | Analysis of ToN-IoT, UNW-NB15, and Edge-IIoT Datasets Using DL in Cybersecurity for IoT |
| title_sort | analysis of ton iot unw nb15 and edge iiot datasets using dl in cybersecurity for iot |
| topic | DenseNet inception time cyber security malware detection ToN-IoT dataset UNSW2015 dataset |
| url | https://www.mdpi.com/2076-3417/12/19/9572 |
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