Academic Journal
STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent.
| Τίτλος: | STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent. |
|---|---|
| Συγγραφείς: | Hezekiah, James Deva Koresh, Duraisamy, Usha Nandini, Nallusamy, Kalaichelvi, Ramalingam, Avudaiammal, Chandran, Saranya, Thiyagupriyadharsan, Murugesan Rajeswari, Selvaraju, Periasamy, Maheswar, Rajagopal |
| Πηγή: | Sensors (14248220); Mar2025, Vol. 25 Issue 6, p1852, 25p |
| Θεματικοί όροι: | Pattern recognition systems, Feature extraction, Anomaly detection (Computer security), Sequential analysis, Internet of things, Intrusion detection systems (Computer security) |
| Περίληψη: | The rapid evolution of IoT environment in medical and industrial applications has led to an increase in network vulnerabilities, making an intrusion detection system a critical requirement. Existing methods often struggle in capturing complex and irregular patterns from dynamic intrusion data, making them not suitable for different IoT applications. To address these limitations, this work proposes STID-Net that integrated customized convolutional kernels for spatial feature extraction and LSTM layers for temporal sequence modelling. Unlike traditional models, STID-Net has an improved ability to identify irregular patterns in dynamic datasets. This work is also equipped with an attention mechanism for enhancing the detection of long-term dependencies in intrusion patterns. The STID-Net is also experimented with the MBGD and SGD optimizers, and we are satisfied with the performance of the SGD optimizer in both the IoMT and IIoT datasets. The SGD optimized model provides a faster convergence and better weight adjustments for handling noisy datasets, making it robust and scalable for diverse IoT applications. This experimental work demonstrates an accuracy of 97.14% and 97.85% with the MBGD optimizer in the IoMT and IIoT datasets, while it attained 98.58% and 99.15% with SGD optimization, respectively. The proposed methodology also outperforms the standalone CNN and LSTM models incorporated with both optimizers, and the result indicates the robustness and scalability of STID-Net in medical and industrial applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Sensors (14248220) is the property of MDPI 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=14248220&ISBN=&volume=25&issue=6&date=20250315&spage=1852&pages=1852-1876&title=Sensors (14248220)&atitle=STID-Net%3A%20Optimizing%20Intrusion%20Detection%20in%20IoT%20with%20Gradient%20Descent.&aulast=Hezekiah%2C%20James%20Deva%20Koresh&id=DOI:10.3390/s25061852 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
|---|---|
| Header | DbId: edb DbLabel: Complementary Index An: 184099250 RelevancyScore: 994 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 993.543334960938 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hezekiah%2C+James+Deva+Koresh%22">Hezekiah, James Deva Koresh</searchLink><br /><searchLink fieldCode="AR" term="%22Duraisamy%2C+Usha+Nandini%22">Duraisamy, Usha Nandini</searchLink><br /><searchLink fieldCode="AR" term="%22Nallusamy%2C+Kalaichelvi%22">Nallusamy, Kalaichelvi</searchLink><br /><searchLink fieldCode="AR" term="%22Ramalingam%2C+Avudaiammal%22">Ramalingam, Avudaiammal</searchLink><br /><searchLink fieldCode="AR" term="%22Chandran%2C+Saranya%22">Chandran, Saranya</searchLink><br /><searchLink fieldCode="AR" term="%22Thiyagupriyadharsan%2C+Murugesan+Rajeswari%22">Thiyagupriyadharsan, Murugesan Rajeswari</searchLink><br /><searchLink fieldCode="AR" term="%22Selvaraju%2C+Periasamy%22">Selvaraju, Periasamy</searchLink><br /><searchLink fieldCode="AR" term="%22Maheswar%2C+Rajagopal%22">Maheswar, Rajagopal</searchLink> – Name: TitleSource Label: Source Group: Src Data: Sensors (14248220); Mar2025, Vol. 25 Issue 6, p1852, 25p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Pattern+recognition+systems%22">Pattern recognition systems</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Anomaly+detection+%28Computer+security%29%22">Anomaly detection (Computer security)</searchLink><br /><searchLink fieldCode="DE" term="%22Sequential+analysis%22">Sequential analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Intrusion+detection+systems+%28Computer+security%29%22">Intrusion detection systems (Computer security)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The rapid evolution of IoT environment in medical and industrial applications has led to an increase in network vulnerabilities, making an intrusion detection system a critical requirement. Existing methods often struggle in capturing complex and irregular patterns from dynamic intrusion data, making them not suitable for different IoT applications. To address these limitations, this work proposes STID-Net that integrated customized convolutional kernels for spatial feature extraction and LSTM layers for temporal sequence modelling. Unlike traditional models, STID-Net has an improved ability to identify irregular patterns in dynamic datasets. This work is also equipped with an attention mechanism for enhancing the detection of long-term dependencies in intrusion patterns. The STID-Net is also experimented with the MBGD and SGD optimizers, and we are satisfied with the performance of the SGD optimizer in both the IoMT and IIoT datasets. The SGD optimized model provides a faster convergence and better weight adjustments for handling noisy datasets, making it robust and scalable for diverse IoT applications. This experimental work demonstrates an accuracy of 97.14% and 97.85% with the MBGD optimizer in the IoMT and IIoT datasets, while it attained 98.58% and 99.15% with SGD optimization, respectively. The proposed methodology also outperforms the standalone CNN and LSTM models incorporated with both optimizers, and the result indicates the robustness and scalability of STID-Net in medical and industrial applications. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Sensors (14248220) is the property of MDPI 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edb&AN=184099250 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/s25061852 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 1852 Subjects: – SubjectFull: Pattern recognition systems Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Anomaly detection (Computer security) Type: general – SubjectFull: Sequential analysis Type: general – SubjectFull: Internet of things Type: general – SubjectFull: Intrusion detection systems (Computer security) Type: general Titles: – TitleFull: STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hezekiah, James Deva Koresh – PersonEntity: Name: NameFull: Duraisamy, Usha Nandini – PersonEntity: Name: NameFull: Nallusamy, Kalaichelvi – PersonEntity: Name: NameFull: Ramalingam, Avudaiammal – PersonEntity: Name: NameFull: Chandran, Saranya – PersonEntity: Name: NameFull: Thiyagupriyadharsan, Murugesan Rajeswari – PersonEntity: Name: NameFull: Selvaraju, Periasamy – PersonEntity: Name: NameFull: Maheswar, Rajagopal IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 14248220 Numbering: – Type: volume Value: 25 – Type: issue Value: 6 Titles: – TitleFull: Sensors (14248220) Type: main |
| ResultId | 1 |