A secure framework for the Internet of Things anomalies using machine learning
Abstract The Internet of Things (IoT) revolutionises modern technology, offering unprecedented opportunities for connectivity and automation. However, the increased adoption of IoT devices introduces substantial security vulnerabilities, necessitating effective anomaly detection frameworks. This Pap...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer
2024-12-01
|
| Series: | Discover Internet of Things |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43926-024-00088-z |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846112292360421376 |
|---|---|
| author | Vijay Prakash Olukayode Odedina Ajay Kumar Lalit Garg Seema Bawa |
| author_facet | Vijay Prakash Olukayode Odedina Ajay Kumar Lalit Garg Seema Bawa |
| author_sort | Vijay Prakash |
| collection | DOAJ |
| description | Abstract The Internet of Things (IoT) revolutionises modern technology, offering unprecedented opportunities for connectivity and automation. However, the increased adoption of IoT devices introduces substantial security vulnerabilities, necessitating effective anomaly detection frameworks. This Paper proposes a secure IoT anomaly detection framework by utilising four machine learning algorithms such as: Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), and Gaussian Naive Bayes (GNB). By generating synthetic datasets with induced anomalies, the framework employs AWS IoT Core infrastructure and Python-based analysis to identify irregularities in device performance. The proposed framework achieved a high detection accuracy ranging from 91 to 98% across the tested algorithms, with CART showing the best performance. Key performance metrics, including precision, recall, and F1-score, confirmed the model's reliability in distinguishing between normal and anomalous IoT data. Experimental results demonstrate superior detection accuracy across all methods, validating the robustness of the proposed approach. This research offers a scalable solution for IoT security, paving the way for improved anomaly detection and mitigation strategies in connected environments. The integration of machine learning algorithms with IoT infrastructure allows for real-time monitoring and proactive anomaly detection in diverse IoT applications. The proposed framework enhances security measures and contributes to the overall reliability and efficiency of connected systems. |
| format | Article |
| id | doaj-art-b3fdcdda639441478fdad469e07cf09c |
| institution | Kabale University |
| issn | 2730-7239 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Internet of Things |
| spelling | doaj-art-b3fdcdda639441478fdad469e07cf09c2024-12-22T12:42:40ZengSpringerDiscover Internet of Things2730-72392024-12-014113210.1007/s43926-024-00088-zA secure framework for the Internet of Things anomalies using machine learningVijay Prakash0Olukayode Odedina1Ajay Kumar2Lalit Garg3Seema Bawa4University of MaltaUniversity of LiverpoolFaculty of Information Technology and Engineering, Gopal Narayan Singh UniversityUniversity of MaltaThapar Institute of Engineering & TechnologyAbstract The Internet of Things (IoT) revolutionises modern technology, offering unprecedented opportunities for connectivity and automation. However, the increased adoption of IoT devices introduces substantial security vulnerabilities, necessitating effective anomaly detection frameworks. This Paper proposes a secure IoT anomaly detection framework by utilising four machine learning algorithms such as: Logistic Regression (LR), Linear Discriminant Analysis (LDA), Classification and Regression Trees (CART), and Gaussian Naive Bayes (GNB). By generating synthetic datasets with induced anomalies, the framework employs AWS IoT Core infrastructure and Python-based analysis to identify irregularities in device performance. The proposed framework achieved a high detection accuracy ranging from 91 to 98% across the tested algorithms, with CART showing the best performance. Key performance metrics, including precision, recall, and F1-score, confirmed the model's reliability in distinguishing between normal and anomalous IoT data. Experimental results demonstrate superior detection accuracy across all methods, validating the robustness of the proposed approach. This research offers a scalable solution for IoT security, paving the way for improved anomaly detection and mitigation strategies in connected environments. The integration of machine learning algorithms with IoT infrastructure allows for real-time monitoring and proactive anomaly detection in diverse IoT applications. The proposed framework enhances security measures and contributes to the overall reliability and efficiency of connected systems.https://doi.org/10.1007/s43926-024-00088-zInternet of ThingsMachine learningClassification modelsAnomaliesSecurity frameworkEnsembling |
| spellingShingle | Vijay Prakash Olukayode Odedina Ajay Kumar Lalit Garg Seema Bawa A secure framework for the Internet of Things anomalies using machine learning Discover Internet of Things Internet of Things Machine learning Classification models Anomalies Security framework Ensembling |
| title | A secure framework for the Internet of Things anomalies using machine learning |
| title_full | A secure framework for the Internet of Things anomalies using machine learning |
| title_fullStr | A secure framework for the Internet of Things anomalies using machine learning |
| title_full_unstemmed | A secure framework for the Internet of Things anomalies using machine learning |
| title_short | A secure framework for the Internet of Things anomalies using machine learning |
| title_sort | secure framework for the internet of things anomalies using machine learning |
| topic | Internet of Things Machine learning Classification models Anomalies Security framework Ensembling |
| url | https://doi.org/10.1007/s43926-024-00088-z |
| work_keys_str_mv | AT vijayprakash asecureframeworkfortheinternetofthingsanomaliesusingmachinelearning AT olukayodeodedina asecureframeworkfortheinternetofthingsanomaliesusingmachinelearning AT ajaykumar asecureframeworkfortheinternetofthingsanomaliesusingmachinelearning AT lalitgarg asecureframeworkfortheinternetofthingsanomaliesusingmachinelearning AT seemabawa asecureframeworkfortheinternetofthingsanomaliesusingmachinelearning AT vijayprakash secureframeworkfortheinternetofthingsanomaliesusingmachinelearning AT olukayodeodedina secureframeworkfortheinternetofthingsanomaliesusingmachinelearning AT ajaykumar secureframeworkfortheinternetofthingsanomaliesusingmachinelearning AT lalitgarg secureframeworkfortheinternetofthingsanomaliesusingmachinelearning AT seemabawa secureframeworkfortheinternetofthingsanomaliesusingmachinelearning |