Network intrusion detection system by applying ensemble model for smart home.
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| Title: | Network intrusion detection system by applying ensemble model for smart home. |
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| Authors: | Amru, Malothu1 malothuamru@gmail.com, Kannan, Raju Jagadeesh2 jagadeeshkannan.r@vit.ac.in, Ganesh, Enthrakandi Narasimhan3 enganesh50@gmail.com, Muthumarilakshmi, Surulivelu4 smlakshmi74@gmail.com, Padmanaban, Kuppan5 padmanaban.k@yahoo.com, Jeyapriya, Jeyaprakash6 priyacseme@gmail.com, Murugan, Subbiah7 smuresjur@gmail.com |
| Source: | International Journal of Electrical & Computer Engineering (2088-8708). Jun2024, Vol. 14 Issue 3, p3485-3494. 10p. |
| Subjects: | Smart homes, HTTP (Computer network protocol), Boosting algorithms, Smart devices, Domestic architecture, Internet of things |
| Abstract: | The exponential advancements in recent technologies for surveillance become an important part of life. Though the internet of things (IoT) has gained more attention to develop smart infrastructure, it also provides a large attack surface for intruders. Therefore, it requires identifying the attacks as soon as possible to provide a secure environment. In this work, the network intrusion detection system, by applying the ensemble model (NIDSE) for Smart Homes is designed to identify the attacks in the smart home devices. The problem of classifying attacks is considered a classification predictive modeling using eXtreme gradient boosting (XGBoosting). It is an ensemble approach where the models are added sequentially to correct the errors until no further improvements or high performance can be made. The performance of the NIDSE is tested on the IoT network intrusion (IoT-NI) dataset. It has various types of network attacks, including host discovery, synchronized sequence number (SYN), acknowledgment (ACK), and hypertext transfer protocol (HTTP) flooding. Results from the crossvalidation approach show that the XGBoosting classifier classifies the nine attacks with micro average precision of 94% and macro average precision of 85%. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 177892544 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Network intrusion detection system by applying ensemble model for smart home. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Amru%2C+Malothu%22">Amru, Malothu</searchLink><relatesTo>1</relatesTo><i> malothuamru@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kannan%2C+Raju+Jagadeesh%22">Kannan, Raju Jagadeesh</searchLink><relatesTo>2</relatesTo><i> jagadeeshkannan.r@vit.ac.in</i><br /><searchLink fieldCode="AR" term="%22Ganesh%2C+Enthrakandi+Narasimhan%22">Ganesh, Enthrakandi Narasimhan</searchLink><relatesTo>3</relatesTo><i> enganesh50@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Muthumarilakshmi%2C+Surulivelu%22">Muthumarilakshmi, Surulivelu</searchLink><relatesTo>4</relatesTo><i> smlakshmi74@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Padmanaban%2C+Kuppan%22">Padmanaban, Kuppan</searchLink><relatesTo>5</relatesTo><i> padmanaban.k@yahoo.com</i><br /><searchLink fieldCode="AR" term="%22Jeyapriya%2C+Jeyaprakash%22">Jeyapriya, Jeyaprakash</searchLink><relatesTo>6</relatesTo><i> priyacseme@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Murugan%2C+Subbiah%22">Murugan, Subbiah</searchLink><relatesTo>7</relatesTo><i> smuresjur@gmail.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Electrical+%26+Computer+Engineering+%282088-8708%29%22">International Journal of Electrical & Computer Engineering (2088-8708)</searchLink>. Jun2024, Vol. 14 Issue 3, p3485-3494. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Smart+homes%22">Smart homes</searchLink><br /><searchLink fieldCode="DE" term="%22HTTP+%28Computer+network+protocol%29%22">HTTP (Computer network protocol)</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Smart+devices%22">Smart devices</searchLink><br /><searchLink fieldCode="DE" term="%22Domestic+architecture%22">Domestic architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The exponential advancements in recent technologies for surveillance become an important part of life. Though the internet of things (IoT) has gained more attention to develop smart infrastructure, it also provides a large attack surface for intruders. Therefore, it requires identifying the attacks as soon as possible to provide a secure environment. In this work, the network intrusion detection system, by applying the ensemble model (NIDSE) for Smart Homes is designed to identify the attacks in the smart home devices. The problem of classifying attacks is considered a classification predictive modeling using eXtreme gradient boosting (XGBoosting). It is an ensemble approach where the models are added sequentially to correct the errors until no further improvements or high performance can be made. The performance of the NIDSE is tested on the IoT network intrusion (IoT-NI) dataset. It has various types of network attacks, including host discovery, synchronized sequence number (SYN), acknowledgment (ACK), and hypertext transfer protocol (HTTP) flooding. Results from the crossvalidation approach show that the XGBoosting classifier classifies the nine attacks with micro average precision of 94% and macro average precision of 85%. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Electrical & Computer Engineering (2088-8708) is the property of Institute of Advanced Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.11591/ijece.v14i3.pp3485-3494 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 3485 Subjects: – SubjectFull: Smart homes Type: general – SubjectFull: HTTP (Computer network protocol) Type: general – SubjectFull: Boosting algorithms Type: general – SubjectFull: Smart devices Type: general – SubjectFull: Domestic architecture Type: general – SubjectFull: Internet of things Type: general Titles: – TitleFull: Network intrusion detection system by applying ensemble model for smart home. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Amru, Malothu – PersonEntity: Name: NameFull: Kannan, Raju Jagadeesh – PersonEntity: Name: NameFull: Ganesh, Enthrakandi Narasimhan – PersonEntity: Name: NameFull: Muthumarilakshmi, Surulivelu – PersonEntity: Name: NameFull: Padmanaban, Kuppan – PersonEntity: Name: NameFull: Jeyapriya, Jeyaprakash – PersonEntity: Name: NameFull: Murugan, Subbiah IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 20888708 Numbering: – Type: volume Value: 14 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Electrical & Computer Engineering (2088-8708) Type: main |
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