A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models
<p dir="ltr">The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can prote...
Saved in:
| Main Author: | |
|---|---|
| Other Authors: | , , , , |
| Published: |
2024
|
| Subjects: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1864513548722372608 |
|---|---|
| author | Osama Bassam J. Rabie (21323741) |
| author2 | Shitharth Selvarajan (14157976) Tawfiq Hasanin (14157965) Abdulrhman M. Alshareef (17541279) C. K. Yogesh (21323744) Mueen Uddin (4903510) |
| author2_role | author author author author author |
| author_facet | Osama Bassam J. Rabie (21323741) Shitharth Selvarajan (14157976) Tawfiq Hasanin (14157965) Abdulrhman M. Alshareef (17541279) C. K. Yogesh (21323744) Mueen Uddin (4903510) |
| author_role | author |
| dc.creator.none.fl_str_mv | Osama Bassam J. Rabie (21323741) Shitharth Selvarajan (14157976) Tawfiq Hasanin (14157965) Abdulrhman M. Alshareef (17541279) C. K. Yogesh (21323744) Mueen Uddin (4903510) |
| dc.date.none.fl_str_mv | 2024-01-03T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1038/s41598-024-51154-z |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_novel_IoT_intrusion_detection_framework_using_Decisive_Red_Fox_optimization_and_descriptive_back_propagated_radial_basis_function_models/29022152 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Control engineering, mechatronics and robotics Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning Internet of Things (IoT) Cybersecurity Intrusion Detection Systems (IDS) Machine Learning |
| dc.title.none.fl_str_mv | A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can protect IoT devices from the most harmful attacks as a security tool. Nevertheless, the time and detection efficiencies of conventional intrusion detection methods need to be more accurate. The main contribution of this paper is to develop a simple as well as intelligent security framework for protecting IoT from cyber-attacks. For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. It also supports increasing the training speed and reducing the error rate of the classifier. Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. Here, the proposed DRF-DBRF security model's performance is validated and tested using five different and popular IoT benchmarking datasets. Finally, the results are compared with the previous anomaly detection approaches by using various evaluation parameters.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-024-51154-z" target="_blank">https://dx.doi.org/10.1038/s41598-024-51154-z</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_33000fc5453569f2653f080d27b96a8e |
| identifier_str_mv | 10.1038/s41598-024-51154-z |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29022152 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function modelsOsama Bassam J. Rabie (21323741)Shitharth Selvarajan (14157976)Tawfiq Hasanin (14157965)Abdulrhman M. Alshareef (17541279)C. K. Yogesh (21323744)Mueen Uddin (4903510)EngineeringControl engineering, mechatronics and roboticsElectrical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesArtificial intelligenceCybersecurity and privacyData management and data scienceDistributed computing and systems softwareMachine learningInternet of Things (IoT)CybersecurityIntrusion Detection Systems (IDS)Machine Learning<p dir="ltr">The Internet of Things (IoT) is extensively used in modern-day life, such as in smart homes, intelligent transportation, etc. However, the present security measures cannot fully protect the IoT due to its vulnerability to malicious assaults. Intrusion detection can protect IoT devices from the most harmful attacks as a security tool. Nevertheless, the time and detection efficiencies of conventional intrusion detection methods need to be more accurate. The main contribution of this paper is to develop a simple as well as intelligent security framework for protecting IoT from cyber-attacks. For this purpose, a combination of Decisive Red Fox (DRF) Optimization and Descriptive Back Propagated Radial Basis Function (DBRF) classification are developed in the proposed work. The novelty of this work is, a recently developed DRF optimization methodology incorporated with the machine learning algorithm is utilized for maximizing the security level of IoT systems. First, the data preprocessing and normalization operations are performed to generate the balanced IoT dataset for improving the detection accuracy of classification. Then, the DRF optimization algorithm is applied to optimally tune the features required for accurate intrusion detection and classification. It also supports increasing the training speed and reducing the error rate of the classifier. Moreover, the DBRF classification model is deployed to categorize the normal and attacking data flows using optimized features. Here, the proposed DRF-DBRF security model's performance is validated and tested using five different and popular IoT benchmarking datasets. Finally, the results are compared with the previous anomaly detection approaches by using various evaluation parameters.</p><h2>Other Information</h2><p dir="ltr">Published in: Scientific Reports<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1038/s41598-024-51154-z" target="_blank">https://dx.doi.org/10.1038/s41598-024-51154-z</a></p>2024-01-03T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1038/s41598-024-51154-zhttps://figshare.com/articles/journal_contribution/A_novel_IoT_intrusion_detection_framework_using_Decisive_Red_Fox_optimization_and_descriptive_back_propagated_radial_basis_function_models/29022152CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/290221522024-01-03T03:00:00Z |
| spellingShingle | A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models Osama Bassam J. Rabie (21323741) Engineering Control engineering, mechatronics and robotics Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning Internet of Things (IoT) Cybersecurity Intrusion Detection Systems (IDS) Machine Learning |
| status_str | publishedVersion |
| title | A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models |
| title_full | A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models |
| title_fullStr | A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models |
| title_full_unstemmed | A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models |
| title_short | A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models |
| title_sort | A novel IoT intrusion detection framework using Decisive Red Fox optimization and descriptive back propagated radial basis function models |
| topic | Engineering Control engineering, mechatronics and robotics Electrical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning Internet of Things (IoT) Cybersecurity Intrusion Detection Systems (IDS) Machine Learning |