利用 LLM 辅助判断联合检测在线对话中的欺诈与概念漂移
Joint Detection of Fraud and Concept Drift inOnline Conversations with LLM-Assisted Judgment
AI总结：
本文提出两阶段框架，先用集成分类器识别可疑对话，再通过 OCDD 与 LLM 判断概念漂移属于欺诈操纵还是合法话题转换，从而提升实时欺诈检测的准确性和可解释性。
摘要：
在数字通信平台中检测虚假交互仍是一项具有挑战性且尚未得到充分解决的问题。这些交互可能表现为无害的垃圾信息，也可能升级为复杂的诈骗企图，因此很难在早期标记恶意意图。传统检测方法通常依赖静态异常检测技术，无法适应动态的对话变化。一个关键局限是将良性的话题转换（即概念漂移）误解为欺诈行为，从而导致误报或漏报威胁。我们提出一种两阶段检测框架：首先使用定制的集成分类模型识别可疑对话。为提高检测可靠性，我们加入概念漂移分析步骤，采用单类漂移检测器（One Class Drift Detector，OCDD）来分离被标记对话中的会话变化。当检测到漂移时，由大型语言模型（LLM）评估该变化表明的是欺诈操纵还是合法的话题转换。在未发现漂移的情况下，该行为被推断为类垃圾信息行为。我们使用社会工程聊天场景数据集验证了该框架，并证明其在提高实时欺诈检测的准确性和可解释性方面具有实际优势。为说明相关权衡，我们将这种模块化方法与使用不同语言模型执行检测和判断的 Dual LLM 基线进行了比较。
英文摘要：
Detecting fake interactions in digital communication platforms remains a challenging and insufficiently addressed problem. These interactions may appear as harmless spam or escalate into sophisticated scam attempts, making it difficult to flag malicious intent early. Traditional detection methods often rely on static anomaly detection techniques that fail to adapt to dynamic conversational shifts. One key limitation is the misinterpretation of benign topic transitions referred to as concept drift as fraudulent behavior, leading to either false alarms or missed threats. We propose a two stage detection framework that first identifies suspicious conversations using a tailored ensemble classification model. To improve the reliability of detection, we incorporate a concept drift analysis step using a One Class Drift Detector (OCDD) to isolate conversational shifts within flagged dialogues. When drift is detected, a large language model (LLM) assesses whether the shift indicates fraudulent manipulation or a legitimate topic change. In cases where no drift is found, the behavior is inferred to be spam like. We validate our framework using a dataset of social engineering chat scenarios and demonstrate its practical advantages in improving both accuracy and interpretability for real time fraud detection. To contextualize the trade offs, we compare our modular approach against a Dual LLM baseline that performs detection and judgment using different language models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
