ArticlePeerJ. Computer science2025
Ensemble techniques for detecting profile cloning attacks in online social networks.
Article in PeerJ. Computer science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Detecting cloned and impersonated profiles on online social networks (OSNs) has become an increasingly critical challenge, particularly with the proliferation of AI-generated content that closely emulates human communication patterns. Traditional identity deception detection methods are proving inadequate against adversaries who exploit large language models (LLMs) to craft syntactically accurate and semantically plausible fake profiles. This article focuses on the detection of profile cloning on LinkedIn by introducing a multi-stage, content-based detection framework that classifies profiles into four distinct categories: legitimate profiles, human-cloned profiles, LLM-generated legitimate profiles, and LLM-generated cloned profiles. The proposed framework integrates multiple analytical layers, including semantic representation learning through attention-based section embedding aggregation, linguistic style modeling using stylometric-perplexity features, anomaly scoring
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