Kernel Metadata Declaration — on demand
ISO 26324 asks a DOI Registration Agency to produce a Kernel Metadata Declaration for every DOI it issues. Give any DOI: its public record becomes a record in the Smart Scholars DOI Metadata Format 1.0, checked against every rule, and declared as the Kernel XML.
✓ Meets every rule of the format
The record below is complete and every controlled value is a DOI Attribute Value Set 2.3 spelling. The Declaration on the right follows DOIMetadataKernel.xsd element by element.
What the DOI identifies
Blockchain-Assisted Federated Learning for Cybersecurity: Combining Isolation Forest, Variational Autoencoders, and Differential Privacy
10.46243/jst.2025.v10.i02.pp95-107 · JournalArticle — an article in a journal · Digital · Visual · Language
Published 2025-02-28
Part of Journal of Science & Technology · ISSN 2456-5660 · vol. 10 · no. 2 · pp. 95–107
Principal agents
- Durga Praveen Devi (author → Author)
- Naga Sushma Allur (author → Author)
- Koteswararao Dondapati (author → Author)
- Himabindu Chetlapalli (author → Author)
- Sharadha Kodadi (author → Author)
- Aravindhan Kurunthachalam (author → Author)
- Longman Publishers (publisher → Publisher)
Also in the record, outside the Kernel: the abstract, the licence, 2 links, 19 references. Source: Crossref (member 25296), registered 2025-08-26, last deposited 2026-09-07.
System metadata — ISO 26324:2025, Annex B · DOI Handbook 10.1
Each element by the standard's name and the Handbook's (in grey), read off the record.
| DOI Name DOI name | 10.46243/jst.2025.v10.i02.pp95-107 |
| Referent Type referentType | Creation |
| Referent Sub-Type referentSubType | JournalArticle — an article in a journal |
| Referent Name(s) referentName(s) | Blockchain-Assisted Federated Learning for Cybersecurity: Combining Isolation Forest, Variational Autoencoders, and Differential Privacy (PrincipalTitle) |
| Basic Metadata basicMetadata | author: Durga Praveen Devi author: Naga Sushma Allur author: Koteswararao Dondapati author: Himabindu Chetlapalli author: Sharadha Kodadi author: Aravindhan Kurunthachalam publisher: Longman Publishers published: 2025-02-28 part of: Journal of Science & Technology · ISSN 2456-5660 · vol. 10 · no. 2 · pp. 95–107 form: Digital · Visual · Language |
| Referent Identifier(s) alternateIdentifier(s) | none besides the DOI |
| Registration Authority registrationAuthorityCode | Crossref — issued by Crossref (member 25296); held here as a copy |
| Created Date issueDate | 2025-08-26 |
| relatedIdentifiers | none needed — the descriptive metadata is in this record |
The record
{
"format": "smartscholars-doi-metadata/1.0",
"doi": "10.46243/jst.2025.v10.i02.pp95-107",
"referent": "Creation",
"type": "JournalArticle",
"structural_type": "Digital",
"modes": [
"Visual"
],
"characters": [
"Language"
],
"titles": [
{
"value": "Blockchain-Assisted Federated Learning for Cybersecurity: Combining Isolation Forest, Variational Autoencoders, and Differential Privacy",
"type": "PrincipalTitle"
}
],
"identifiers": [
{
"type": "DOI",
"value": "10.46243/jst.2025.v10.i02.pp95-107"
}
],
"agents": [
{
"role": "author",
"name": {
"given": "",
"family": "Durga Praveen Devi"
},
"sequence": "first"
},
{
"role": "author",
"name": {
"given": "",
"family": "Naga Sushma Allur"
},
"sequence": "additional"
},
{
"role": "author",
"name": {
"given": "",
"family": "Koteswararao Dondapati"
},
"sequence": "additional"
},
{
"role": "author",
"name": {
"given": "",
"family": "Himabindu Chetlapalli"
},
"sequence": "additional"
},
{
"role": "author",
"name": {
"given": "",
"family": "Sharadha Kodadi"
},
"sequence": "additional"
},
{
"role": "author",
"name": {
"given": "",
"family": "Aravindhan Kurunthachalam"
},
"sequence": "additional"
},
{
"role": "publisher",
"name": {
"org": "Longman Publishers"
}
}
],
"dates": {
"published": "2025-02-28",
"date_type": "PublicationDate",
"online": "2025-02-28"
},
"container": {
"type": "Journal",
"titles": [
{
"value": "Journal of Science & Technology",
"type": "PrincipalTitle"
},
{
"value": "J. sci. technol.",
"type": "AbbreviatedTitle"
}
],
"identifiers": [
{
"type": "ISSN",
"value": "2456-5660",
"medium": "electronic"
}
],
"volume": "10",
"issue": "2",
"pages": {
"first": "95",
"last": "107"
}
},
"links": [
{
"url": "https://jst.org.in/index.php/pub/article/view/1180",
"return_type": "text/html",
"primary": true
},
{
"url": "https://jst.org.in/index.php/pub/article/download/1180/954",
"purpose": "text-mining",
"return_type": "application/pdf"
}
],
"abstract": {
"value": "The complexity of the cyber threats dictates the need for strong, privacy-preservingmechanisms for anomaly detection. This paper introduces a new framework called BAFL, anintegration of Isolation Forest and Variational Autoencoders combined with DifferentialPrivacy, for safe and scalable solutions in cybersecurity applications. Federated Learningallows distributed training across numerous clients without exposure of sensitive information,while the blockchain technology introduces trust and integrity in model updates."
},
"license": {
"url": "https://creativecommons.org/licenses/by/4.0",
"start": "2025-02-28",
"applies_to": "unspecified"
},
"references": [
{
"key": "ref1",
"doi": "10.1177/1550147718814471",
"unstructured": "Tao, X., Peng, Y., Zhao, F., Zhao, P., & Wang, Y. (2018). A parallel algorithm for network traffic anomaly detection based on Isolation Forest. International Journal of Distributed Sensor Networks, 14(11), 1550147718814471"
},
{
"key": "ref2",
"doi": "10.3390/app8122663",
"unstructured": "Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., & Ilie-Zudor, E. (2018). Chained anomaly detection models for federated learning: An intrusion detection case study. Applied Sciences, 8(12), 2663"
},
{
"key": "ref3",
"doi": "10.1109/lsens.2018.2879990",
"unstructured": "Abdulhammed, R., Faezipour, M., Abuzneid, A., & AbuMallouh, A. (2018). Deep and machine learning approaches for anomaly-based intrusion detection of imbalanced network traffic. IEEE sensors letters, 3(1), 1-4"
},
{
"key": "ref4",
"doi": "10.1038/s42256-021-00337-8",
"unstructured": "Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., & Passerat-Palmbach, J. (2018). A generic framework for privacy preserving deep learning. arXiv preprint arXiv:1811.04017"
},
{
"key": "ref5",
"unstructured": "Chen, Q., Xiang, C., Xue, M., Li, B., Borisov, N., Kaarfar, D., & Zhu, H. (2018). Differentially private data generative models. arXiv preprint arXiv:1812.02274"
},
{
"key": "ref6",
"unstructured": "Alotaibi, A. (2018). Wisdom of the machines: federated learning using OPAL (Doctoral dissertation, Massachusetts Institute of Technology)"
},
{
"key": "ref7",
"doi": "10.1109/tpds.2020.3044223",
"unstructured": "Shayan, M., Fung, C., Yoon, C. J., & Beschastnikh, I. (2018). Biscotti: A ledger for private and secure peer-to-peer machine learning. arXiv preprint arXiv:1811.09904"
},
{
"key": "ref8",
"unstructured": "Ning, X., Zheng, Y., Jiang, Z., Wang, Y., Yang, H., & Huang, J. (2018). A Bayesian nonparametric topic model with variational auto-encoders"
},
{
"key": "ref9",
"doi": "10.23860/thesis-zhang-yazhou-2018",
"unstructured": "Zhang, Y. (2018). Deep generative model for multi-class imbalanced learning"
},
{
"key": "ref10",
"doi": "10.1007/978-3-319-71767-8_93",
"unstructured": "Narayana, P. (2018). A prototype to detect anomalies using machine learning algorithms and deep neural network. In Computational Vision and Bio Inspired Computing (pp. 1084- 1094). Springer International Publishing"
},
{
"key": "ref11",
"doi": "10.14778/3229863.3236266",
"unstructured": "Hynes, N., Dao, D., Yan, D., Cheng, R., & Song, D. (2018). A demonstration of sterling: a privacypreserving data marketplace. Proceedings of the VLDB Endowment, 11(12), 2086-2089"
},
{
"key": "ref12",
"unstructured": "Geyer, R. C., Klein, T., & Nabi, M. (2017). Differentially private federated learning: A client level perspective. arXiv preprint arXiv:1712.07557"
},
{
"key": "ref13",
"doi": "10.1109/smartworld.2018.00106",
"unstructured": "Zhou, W., Li, Y., Chen, S., & Ding, B. (2018, October). Real-time data processing architecture for multi-robots based on differential federated learning. In 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/ IOP/SCI) (pp. 462-471). IEEE"
},
{
"key": "ref14",
"unstructured": "Hartmann, F. (2018). Federated learning. Freie Universität Berlin"
},
{
"key": "ref15",
"unstructured": "McMahan, H. B., Andrew, G., Erlingsson, U., Chien, S., Mironov, I., Papernot, N., & Kairouz, P. (2018). A general approach to adding differential privacy to iterative training procedures. arXiv preprint arXiv:1812.06210. DOI:https://doi.org/10.46243/jst.2025.v10.i02.pp95- 10751 Durga Praveen Devi, Naga Sushma Allur, Koteswararao Dondapati, Himabindu Chetlapalli, Sharadha Kodadi, Aravindhan Kurunthachalam: Blockchain-Assisted Federated Learning for Cybersecurity: Combining Isolation Forest, Variational Autoencoders, and Differential Privacy"
},
{
"key": "ref16",
"doi": "10.1109/tifs.2016.2607691",
"unstructured": "Zhang, T., & Zhu, Q. (2017). Dynamic Differential Privacy for ADMM-Based Distributed Classification Learning. IEEE Transactions on Information Forensics and Security, 12(1), 172–"
},
{
"key": "ref17",
"unstructured": "Harder, F., Köhler, J., Welling, M., & Park, M. (2018). DP-MAC: The Differentially Private Method of Auxiliary Coordinates for Deep Learning"
},
{
"key": "ref18",
"doi": "10.1007/s11227-018-2385-7",
"unstructured": "Malomo, O., Rawat, D. B., & Garuba, M. (2018). Next-generation cybersecurity through a blockchain-enabled federated cloud framework. The Journal of Supercomputing, 74(10), 5099–5126"
},
{
"key": "ref19",
"unstructured": "https://www.kaggle.com/datasets/ ameerhamza123/intrusion-detection-dataset"
}
],
"record": {
"registrant": "Longman Publishers",
"registered": "2025-08-26",
"updated": "2026-09-07",
"issue_number": 1,
"source": "crossref-api",
"source_agency": "Crossref (member 25296)"
}
}The Kernel Metadata Declaration
DOIMetadataKernel.xsd · namespace http://www.doi.org/2010/DOISchema · Attribute Value Sets 2.3 · draft — the agency's DOI name is filled in on accreditation
<?xml version="1.0" encoding="UTF-8"?>
<!-- DRAFT declaration: Smart Scholars is not yet a DOI Registration Agency. registrationAgencyDoiName is a marker (10.0/ is no RA's prefix) and is filled in on accreditation. -->
<kernelMetadata xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.doi.org/2010/DOISchema https://www.doi.org/doi_schemas/DOIMetadataKernel.xsd" xmlns="http://www.doi.org/2010/DOISchema">
<referentDoiName>10.46243/jst.2025.v10.i02.pp95-107</referentDoiName>
<primaryReferentType>Creation</primaryReferentType>
<registrationAgencyDoiName>10.0/smart-scholars-draft</registrationAgencyDoiName>
<issueDate>2026-10-04</issueDate>
<issueNumber>1</issueNumber>
<referentCreation>
<name>
<value>Blockchain-Assisted Federated Learning for Cybersecurity: Combining Isolation Forest, Variational Autoencoders, and Differential Privacy</value>
<type>PrincipalTitle</type>
</name>
<identifier>
<nonUriValue>10.46243/jst.2025.v10.i02.pp95-107</nonUriValue>
<uri returnType="text/html" doesContentNegotiation="true">https://doi.org/10.46243/jst.2025.v10.i02.pp95-107</uri>
<type>DOI</type>
</identifier>
<identifier>
<uri returnType="text/html">https://jst.org.in/index.php/pub/article/view/1180</uri>
<type>URI</type>
</identifier>
<identifier>
<uri>https://jst.org.in/index.php/pub/article/download/1180/954</uri>
<type>URI</type>
</identifier>
<structuralType>Digital</structuralType>
<mode>Visual</mode>
<character>Language</character>
<type>JournalArticle</type>
<principalAgent>
<name>
<value>Durga Praveen Devi</value>
<type>PrincipalName</type>
</name>
<role>Author</role>
</principalAgent>
<principalAgent>
<name>
<value>Naga Sushma Allur</value>
<type>PrincipalName</type>
</name>
<role>Author</role>
</principalAgent>
<principalAgent>
<name>
<value>Koteswararao Dondapati</value>
<type>PrincipalName</type>
</name>
<role>Author</role>
</principalAgent>
<principalAgent>
<name>
<value>Himabindu Chetlapalli</value>
<type>PrincipalName</type>
</name>
<role>Author</role>
</principalAgent>
<principalAgent>
<name>
<value>Sharadha Kodadi</value>
<type>PrincipalName</type>
</name>
<role>Author</role>
</principalAgent>
<principalAgent>
<name>
<value>Aravindhan Kurunthachalam</value>
<type>PrincipalName</type>
</name>
<role>Author</role>
</principalAgent>
<principalAgent>
<name>
<value>Longman Publishers</value>
<type>PrincipalName</type>
</name>
<role>Publisher</role>
</principalAgent>
<linkedCreation>
<name>
<value>Journal of Science & Technology</value>
<type>PrincipalTitle</type>
</name>
<name>
<value>J. sci. technol.</value>
<type>AbbreviatedTitle</type>
</name>
<identifier>
<nonUriValue>2456-5660</nonUriValue>
<type>ISSN</type>
</identifier>
<referentCreationRole>Part</referentCreationRole>
<referentCreationSequenceIdentifier>
<value>10</value>
<type>VolumeNumber</type>
</referentCreationSequenceIdentifier>
<referentCreationSequenceIdentifier>
<value>2</value>
<type>IssueNumber</type>
</referentCreationSequenceIdentifier>
<referentCreationSequenceIdentifier>
<value>95-107</value>
<type>PageNumber</type>
</referentCreationSequenceIdentifier>
</linkedCreation>
<creationDate>
<date>2025-02-28</date>
<creationDateType>PublicationDate</creationDateType>
</creationDate>
</referentCreation>
</kernelMetadata>
