Internet of Things-Based Anomaly Detection Hybrid Framework Simulation Integration of Deep Learning and Blockchain
IoT environments have introduced diverse logistic support services into our lives and communities, in areas such as education, medicine, transportation, and agriculture. However, with new technologies and services, the issue of privacy and data security has become more urgent. Moreover, the rapid ch...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Information |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2078-2489/16/5/406 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850257210328743936 |
|---|---|
| author | Ahmad M. Almasabi Ahmad B. Alkhodre Maher Khemakhem Fathy Eassa Adnan Ahmed Abi Sen Ahmed Harbaoui |
| author_facet | Ahmad M. Almasabi Ahmad B. Alkhodre Maher Khemakhem Fathy Eassa Adnan Ahmed Abi Sen Ahmed Harbaoui |
| author_sort | Ahmad M. Almasabi |
| collection | DOAJ |
| description | IoT environments have introduced diverse logistic support services into our lives and communities, in areas such as education, medicine, transportation, and agriculture. However, with new technologies and services, the issue of privacy and data security has become more urgent. Moreover, the rapid changes in IoT and the capabilities of attacks have highlighted the need for an adaptive and reliable framework. In this study, we applied the proposed simulation to the proposed hybrid framework, making use of deep learning to continue monitoring IoT data; we also used the blockchain association in the framework to log, tackle, manage, and document all of the IoT sensor’s data points. Five sensors were run in a SimPy simulation environment to check and examine our framework’s capability in a real-time IoT environment; deep learning (ANN) and the blockchain technique were integrated to enhance the efficiency of detecting certain attacks (benign, part of a horizontal port scan, attack, C&C, Okiru, DDoS, and file download) and to continue logging all of the IoT sensor data, respectively. The comparison of different machine learning (ML) models showed that the DL outperformed all of them. Interestingly, the evaluation results showed a mature and moderate level of accuracy and precision and reached 97%. Moreover, the proposed framework confirmed superior performance under varied conditions like diverse attack types and network sizes comparing to other approaches. It can improve its performance over time and can detect anomalies in real-time IoT environments. |
| format | Article |
| id | doaj-art-eb89e632c98e44c3be53881a4cf8fd6f |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-eb89e632c98e44c3be53881a4cf8fd6f2025-08-20T01:56:28ZengMDPI AGInformation2078-24892025-05-0116540610.3390/info16050406Internet of Things-Based Anomaly Detection Hybrid Framework Simulation Integration of Deep Learning and BlockchainAhmad M. Almasabi0Ahmad B. Alkhodre1Maher Khemakhem2Fathy Eassa3Adnan Ahmed Abi Sen4Ahmed Harbaoui5Department of Computer Science, Faculty of Computing & Information Technology, King Abdul-Aziz University, Jeddah 21442, Saudi ArabiaDepartment of Information Technology, Faculty of Computer and Information Systems, Islamic University of Madinah, Al-Madinah 42351, Saudi ArabiaDepartment of Computer Science, Faculty of Computing & Information Technology, King Abdul-Aziz University, Jeddah 21442, Saudi ArabiaDepartment of Computer Science, Faculty of Computing & Information Technology, King Abdul-Aziz University, Jeddah 21442, Saudi ArabiaDr. Hussein ElSayyed Research Center, Department of Graduate Studies & Scientific Research, University of Prince Mugrin, Madinah 42241, Saudi ArabiaDepartment of Computer Science, Faculty of Computing & Information Technology, King Abdul-Aziz University, Jeddah 21442, Saudi ArabiaIoT environments have introduced diverse logistic support services into our lives and communities, in areas such as education, medicine, transportation, and agriculture. However, with new technologies and services, the issue of privacy and data security has become more urgent. Moreover, the rapid changes in IoT and the capabilities of attacks have highlighted the need for an adaptive and reliable framework. In this study, we applied the proposed simulation to the proposed hybrid framework, making use of deep learning to continue monitoring IoT data; we also used the blockchain association in the framework to log, tackle, manage, and document all of the IoT sensor’s data points. Five sensors were run in a SimPy simulation environment to check and examine our framework’s capability in a real-time IoT environment; deep learning (ANN) and the blockchain technique were integrated to enhance the efficiency of detecting certain attacks (benign, part of a horizontal port scan, attack, C&C, Okiru, DDoS, and file download) and to continue logging all of the IoT sensor data, respectively. The comparison of different machine learning (ML) models showed that the DL outperformed all of them. Interestingly, the evaluation results showed a mature and moderate level of accuracy and precision and reached 97%. Moreover, the proposed framework confirmed superior performance under varied conditions like diverse attack types and network sizes comparing to other approaches. It can improve its performance over time and can detect anomalies in real-time IoT environments.https://www.mdpi.com/2078-2489/16/5/406IoTdeep learningblockchainattackssecurityprivacy |
| spellingShingle | Ahmad M. Almasabi Ahmad B. Alkhodre Maher Khemakhem Fathy Eassa Adnan Ahmed Abi Sen Ahmed Harbaoui Internet of Things-Based Anomaly Detection Hybrid Framework Simulation Integration of Deep Learning and Blockchain Information IoT deep learning blockchain attacks security privacy |
| title | Internet of Things-Based Anomaly Detection Hybrid Framework Simulation Integration of Deep Learning and Blockchain |
| title_full | Internet of Things-Based Anomaly Detection Hybrid Framework Simulation Integration of Deep Learning and Blockchain |
| title_fullStr | Internet of Things-Based Anomaly Detection Hybrid Framework Simulation Integration of Deep Learning and Blockchain |
| title_full_unstemmed | Internet of Things-Based Anomaly Detection Hybrid Framework Simulation Integration of Deep Learning and Blockchain |
| title_short | Internet of Things-Based Anomaly Detection Hybrid Framework Simulation Integration of Deep Learning and Blockchain |
| title_sort | internet of things based anomaly detection hybrid framework simulation integration of deep learning and blockchain |
| topic | IoT deep learning blockchain attacks security privacy |
| url | https://www.mdpi.com/2078-2489/16/5/406 |
| work_keys_str_mv | AT ahmadmalmasabi internetofthingsbasedanomalydetectionhybridframeworksimulationintegrationofdeeplearningandblockchain AT ahmadbalkhodre internetofthingsbasedanomalydetectionhybridframeworksimulationintegrationofdeeplearningandblockchain AT maherkhemakhem internetofthingsbasedanomalydetectionhybridframeworksimulationintegrationofdeeplearningandblockchain AT fathyeassa internetofthingsbasedanomalydetectionhybridframeworksimulationintegrationofdeeplearningandblockchain AT adnanahmedabisen internetofthingsbasedanomalydetectionhybridframeworksimulationintegrationofdeeplearningandblockchain AT ahmedharbaoui internetofthingsbasedanomalydetectionhybridframeworksimulationintegrationofdeeplearningandblockchain |