采用上下文判定与消息级分析的增强型在线诱拐检测方法
Enhanced Online Grooming Detection Employing Context Determination and Message-Level Analysis
AI总结：
针对现有实时在线诱拐检测手段不足的问题，本文提出结合BERT、RoBERTa消息级分析与含双阈值的上下文判定方法，经跨数据集验证可提升检测准确率与鲁棒性，填补相关研究实践空白。
摘要：
在线诱拐（Online Grooming, OG）是主要针对儿童的普遍网络威胁，诱拐者在社交媒体/即时通讯平台上利用欺骗手段利用儿童的脆弱性实施侵害。这类攻击会造成严重的心理和生理影响，包括使受害者更易遭受二次侵害。现有技术手段存在不足，尤其是端到端加密的普及阻碍了消息监控；当前方案主要聚焦于虐童媒体的特征分析，无法有效实现实时在线诱拐检测。本文指出在线诱拐攻击具有复杂性，需要识别成人与儿童之间的特定沟通模式，提出了一种新方法：采用BERT、RoBERTa等先进模型开展消息级分析，同时引入上下文判定方法对交互参与者进行分类，其中包含参与者重要性阈值与消息重要性阈值设计。该方法通过考虑这类攻击的动态性与多面性，旨在提升在线诱拐检测的准确率与鲁棒性。研究通过跨数据集实验评估了该方法的鲁棒性与通用性，其贡献包括改进了检测方法体系，具备多场景应用潜力，填补了现有研究与实践的空白。
英文摘要：
Online Grooming (OG) is a prevalent threat facing predominately children online, with groomers using deceptive methods to prey on the vulnerability of children on social media/messaging platforms. These attacks can have severe psychological and physical impacts, including a tendency towards revictimization. Current technical measures are inadequate, especially with the advent of end-to-end encryption which hampers message monitoring. Existing solutions focus on the signature analysis of child abuse media, which does not effectively address real-time OG detection. This paper proposes that OG attacks are complex, requiring the identification of specific communication patterns between adults and children. It introduces a novel approach leveraging advanced models such as BERT and RoBERTa for Message-Level Analysis and a Context Determination approach for classifying actor interactions, including the introduction of Actor Significance Thresholds and Message Significance Thresholds. The proposed method aims to enhance accuracy and robustness in detecting OG by considering the dynamic and multi-faceted nature of these attacks. Cross-dataset experiments evaluate the robustness and versatility of our approach. This paper's contributions include improved detection methodologies and the potential for application in various scenarios, addressing gaps in current literature and practices.
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。
