SNAP Framework: Linked Prediction Based Anomaly Prevention With Suspicious Nodes on Social Network Graph

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dc.authoridSaraç, Esra/0000-0002-2503-0084
dc.contributor.authorKilig, Vahide Nida
dc.contributor.authorEssiz, Esra Sarac
dc.date.accessioned2026-02-27T07:33:29Z
dc.date.available2026-02-27T07:33:29Z
dc.date.issued2025
dc.description.abstractIn previous studies, the focus has predominantly been on anomaly detection, with minimal attention given to anomaly prevention. However, anomaly prevention holds greater significance than anomaly detection. Preventing anomalous behavior before it occurs and identifying potential anomalies in advance to enable timely intervention is both challenging and crucial. In this study, a Suspicious Nodes Anomaly Prevention framework for anomaly prevention has been developed. First, a novel K-medoid based Salp Swarm Anomaly Detection method is proposed within the framework. This method reveals unclustered data by applying clustering and determines the boundaries of clusters using a nature-inspired algorithm that optimizes the threshold. Since threshold determination is an optimization problem, it aligns well with nature-inspired algorithms. Additionally, the Enron email dataset was selected as it is a real-world dataset with accessible content information. Initially, content and node features were extracted from the Enron email dataset. The proposed anomaly detection method was then applied separately to each of these features. Nodes identified as anomalous by one feature but normal by others were of particular interest. These nodes were labeled as suspicious nodes, and their connections were analyzed to detect potentially harmful email content. This framework fills a significant gap in the anomaly detection literature by contributing an unprecedented approach to anomaly prevention, offering early intervention capabilities in various sectors by identifying risks in advance. In this study, the proposed framework demonstrates high efficacy in detecting anomalies, achieving a True Positive Rate of 94% in node-based anomaly detection and 78% in content-based anomaly detection, indicating a robust capability for early intervention and risk identification.
dc.identifier.doi10.3897/jucs.152114
dc.identifier.endpage1563
dc.identifier.issn0948-695X
dc.identifier.issn0948-6968
dc.identifier.issue13
dc.identifier.startpage1538
dc.identifier.urihttp://dx.doi.org/10.3897/jucs.152114
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4604
dc.identifier.volume31
dc.identifier.wosWOS:001637639600006
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherGraz Univ Technolgoy, Inst Information Systems Computer Media-IICM
dc.relation.ispartofJournal of Universal Computer Science
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20260302
dc.subjectAnomaly Prevention
dc.subjectLinked Prediction
dc.subjectSocial Network Graph
dc.subjectNature Inspired Algorithms
dc.subjectEnron Dataset
dc.titleSNAP Framework: Linked Prediction Based Anomaly Prevention With Suspicious Nodes on Social Network Graph
dc.typeArticle

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