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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | Anomaly Detection System in Blockchain |
| Authors Name: | Jamuna , Mrs. Rekha B K , Lavanya T M , Preethi K , Prerana S |
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IJRTI_208109
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| Published Paper Id: | IJRTI2511191 |
| Published In: | Volume 10 Issue 11, November-2025 |
| DOI: | |
| Abstract: | Blockchain networks produce massive and continuously expanding sets of transactional data. Subtle deviations within these records often indicate fraud attempts, misuse of resources, or unexpected network behavior. Detecting such anomalies is challenging because blockchain activity evolves rapidly, contains high-dimensional features, and rarely includes labeled instances of malicious patterns. This study presents a multi-layer anomaly detection architecture that integrates supervised learning, unsupervised clustering, and statistical deviation analysis. Random Forest, XGBoost, K-Means, One-Class SVM, and Z-score profiling collectively contribute unique indicators of abnormal behavior, which are merged into a unified hybrid anomaly score. An additional clustering layer categorizes detected anomalies, while severity scoring highlights events requiring urgent attention. Extensive experimentation on enriched blockchain datasets demonstrates that the proposed hybrid approach achieves higher sensitivity to rare anomalies, lower false-alarm rates, and better interpretability compared to single-model methods. The system also incorporates PCA-based visualization and an interactive Streamlit interface for real-time monitoring. Findings show that hybrid learning pipelines are highly effective for securing decentralized ledgers in high-volume cryptocurrency ecosystems. |
| Keywords: | Blockchain networks produce massive and continuously expanding sets of transactional data. Subtle deviations within these records often indicate fraud attempts, misuse of resources, or unexpected network behavior. Detecting such anomalies is challenging because blockchain activity evolves rapidly, contains high-dimensional features, and rarely includes labeled instances of malicious patterns. This study presents a multi-layer anomaly detection architecture that integrates supervised learning, unsupervised clustering, and statistical deviation analysis. Random Forest, XGBoost, K-Means, One-Class SVM, and Z-score profiling collectively contribute unique indicators of abnormal behavior, which are merged into a unified hybrid anomaly score. An additional clustering layer categorizes detected anomalies, while severity scoring highlights events requiring urgent attention. Extensive experimentation on enriched blockchain datasets demonstrates that the proposed hybrid approach achieves higher sensitivity to rare anomalies, lower false-alarm rates, and better interpretability compared to single-model methods. The system also incorporates PCA-based visualization and an interactive Streamlit interface for real-time monitoring. Findings show that hybrid learning pipelines are highly effective for securing decentralized ledgers in high-volume cryptocurrency ecosystems. |
| Cite Article: | "Anomaly Detection System in Blockchain", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 11, page no.b801-b806, November-2025, Available :http://www.ijrti.org/papers/IJRTI2511191.pdf |
| Downloads: | 000204 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2511191
Registration ID:208109
Published In: Volume 10 Issue 11, November-2025
DOI (Digital Object Identifier):
Page No: b801-b806 Country: bengaluru, Karnataka, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2511191 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2511191 |
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