TH-GNN：用于检测大语言模型智能体刷单攻击的异质性时序图神经网络

中文摘要 AI 辅助

大语言模型（LLM）智能体如今可规模化生成逼真的刷单用户画像、流畅评论及连贯评分，系统性突破推荐系统防御机制。仅基于文本的检测器会标记评论嵌入中的语义漂移，但忽略图结构与时序协同特征；仅基于图的检测器虽能利用邻居异常，却无法推理评论语义或LLM生成内容产生的跨模态不一致性。本文提出TH-GNN，这是一种异质性时序图神经网络，具备双层异构图Transformer骨干，对每条边应用按类型和关系划分的注意力机制，并辅以可学习的正弦时序编码。跨模态注意力将结构用户嵌入与冻结的RoBERTa模型生成的评论、商品描述表示相融合，同时基于对数到达间隔时间的门控循环单元（GRU）捕捉时序突发特性。在5类攻击族与4个基准数据集上评估显示，TH-GNN的总体平均F1值达0.870，在Agent4SR攻击上，其性能较最强的纯文本基线模型在最低注入率时分别高出10.9个百分点和11.5个百分点。这些结果表明，联合建模时序、结构与语义信号对检测复杂的LLM驱动型刷单攻击具有有效性。

英文摘要

LLM agents can now generate realistic shilling profiles, fluent reviews, and coherent ratings at scale, systematically defeating recommender-system defenses. Text-only detectors that flag semantic drift in review embeddings are blind to graph structure and temporal coordination, while graph-only detectors that exploit neighborhood anomalies cannot reason over review semantics or the cross-modal inconsistencies produced by LLM-generated content. We propose TH-GNN, a heterogeneous temporal graph neural network with a two-layer Heterogeneous Graph Transformer backbone that applies per-type and per-relation attention augmented with learnable sinusoidal temporal encodings on every edge. Cross-modal attention fuses structural user embeddings with frozen RoBERTa representations of reviews and item descriptions, while a GRU operating over log inter-arrival times captures temporal burstiness. Evaluated across five attack families and four benchmark datasets, TH-GNN achieves a grand-mean F1 score of 0.870, outperforming the strongest text-only baseline on Agent4SR attacks by 10.9 percentage points and 11.5 percentage points at the lowest injection rate. These results demonstrate the effectiveness of jointly modeling temporal, structural, and semantic signals for detecting sophisticated LLM-driven shilling attacks.

发表机构

• JAIN (Deemed to be University)（JAIN（被认定大学））
• Zayed University（扎耶德大学）

机构由 AI 辅助整理，请以论文原文为准。