Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques
<p dir="ltr">The number of Internet of Things (IoT) devices is growing at a fast pace in smart homes, producing large amounts of data, which are mostly transferred over wireless communication channels. However, various IoT devices are vulnerable to different threats, such as cyber-at...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2020
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513511871217664 |
|---|---|
| author | Ameema Zainab (16864263) |
| author2 | Shady S. Refaat (15945006) Othmane Bouhali (8252544) |
| author2_role | author author |
| author_facet | Ameema Zainab (16864263) Shady S. Refaat (15945006) Othmane Bouhali (8252544) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ameema Zainab (16864263) Shady S. Refaat (15945006) Othmane Bouhali (8252544) |
| dc.date.none.fl_str_mv | 2020-07-02T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/info11070344 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Ensemble-Based_Spam_Detection_in_Smart_Home_IoT_Devices_Time_Series_Data_Using_Machine_Learning_Techniques/26134105 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning IoT devices spamicity score machine learning IoT security smart home |
| dc.title.none.fl_str_mv | Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The number of Internet of Things (IoT) devices is growing at a fast pace in smart homes, producing large amounts of data, which are mostly transferred over wireless communication channels. However, various IoT devices are vulnerable to different threats, such as cyber-attacks, fluctuating network connections, leakage of information, etc. Statistical analysis and machine learning can play a vital role in detecting the anomalies in the data, which enhances the security level of the smart home IoT system which is the goal of this paper. This paper investigates the trustworthiness of the IoT devices sending house appliances’ readings, with the help of various parameters such as feature importance, root mean square error, hyper-parameter tuning, etc. A spamicity score was awarded to each of the IoT devices by the algorithm, based on the feature importance and the root mean square error score of the machine learning models to determine the trustworthiness of the device in the home network. A dataset publicly available for a smart home, along with weather conditions, is used for the methodology validation. The proposed algorithm is used to detect the spamicity score of the connected IoT devices in the network. The obtained results illustrate the efficacy of the proposed algorithm to analyze the time series data from the IoT devices for spam detection.</p><h2>Other Information</h2><p dir="ltr">Published in: Information<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/10.3390/info11070344" target="_blank">https://dx.doi.org/10.3390/info11070344</a></p><p dir="ltr">Additional institutions affiliated with: Electrical and Computer Engineering Program - TAMUQ</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_982f56272b7abd670d4f1155efb94285 |
| identifier_str_mv | 10.3390/info11070344 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26134105 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning TechniquesAmeema Zainab (16864263)Shady S. Refaat (15945006)Othmane Bouhali (8252544)Information and computing sciencesCybersecurity and privacyData management and data scienceDistributed computing and systems softwareMachine learningIoT devicesspamicity scoremachine learningIoT securitysmart home<p dir="ltr">The number of Internet of Things (IoT) devices is growing at a fast pace in smart homes, producing large amounts of data, which are mostly transferred over wireless communication channels. However, various IoT devices are vulnerable to different threats, such as cyber-attacks, fluctuating network connections, leakage of information, etc. Statistical analysis and machine learning can play a vital role in detecting the anomalies in the data, which enhances the security level of the smart home IoT system which is the goal of this paper. This paper investigates the trustworthiness of the IoT devices sending house appliances’ readings, with the help of various parameters such as feature importance, root mean square error, hyper-parameter tuning, etc. A spamicity score was awarded to each of the IoT devices by the algorithm, based on the feature importance and the root mean square error score of the machine learning models to determine the trustworthiness of the device in the home network. A dataset publicly available for a smart home, along with weather conditions, is used for the methodology validation. The proposed algorithm is used to detect the spamicity score of the connected IoT devices in the network. The obtained results illustrate the efficacy of the proposed algorithm to analyze the time series data from the IoT devices for spam detection.</p><h2>Other Information</h2><p dir="ltr">Published in: Information<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/10.3390/info11070344" target="_blank">https://dx.doi.org/10.3390/info11070344</a></p><p dir="ltr">Additional institutions affiliated with: Electrical and Computer Engineering Program - TAMUQ</p>2020-07-02T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/info11070344https://figshare.com/articles/journal_contribution/Ensemble-Based_Spam_Detection_in_Smart_Home_IoT_Devices_Time_Series_Data_Using_Machine_Learning_Techniques/26134105CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/261341052020-07-02T03:00:00Z |
| spellingShingle | Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques Ameema Zainab (16864263) Information and computing sciences Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning IoT devices spamicity score machine learning IoT security smart home |
| status_str | publishedVersion |
| title | Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques |
| title_full | Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques |
| title_fullStr | Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques |
| title_full_unstemmed | Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques |
| title_short | Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques |
| title_sort | Ensemble-Based Spam Detection in Smart Home IoT Devices Time Series Data Using Machine Learning Techniques |
| topic | Information and computing sciences Cybersecurity and privacy Data management and data science Distributed computing and systems software Machine learning IoT devices spamicity score machine learning IoT security smart home |