Enhanced Online Grooming Detection Employing Context Determination and Message-Level Analysis

Jake Street, Isibor Ihianle, Funminiyi Olajide, Ahmad Lotfi
{"title":"Enhanced Online Grooming Detection Employing Context Determination and Message-Level Analysis","authors":"Jake Street, Isibor Ihianle, Funminiyi Olajide, Ahmad Lotfi","doi":"arxiv-2409.07958","DOIUrl":null,"url":null,"abstract":"Online Grooming (OG) is a prevalent threat facing predominately children\nonline, with groomers using deceptive methods to prey on the vulnerability of\nchildren on social media/messaging platforms. These attacks can have severe\npsychological and physical impacts, including a tendency towards\nrevictimization. Current technical measures are inadequate, especially with the\nadvent of end-to-end encryption which hampers message monitoring. Existing\nsolutions focus on the signature analysis of child abuse media, which does not\neffectively address real-time OG detection. This paper proposes that OG attacks\nare complex, requiring the identification of specific communication patterns\nbetween adults and children. It introduces a novel approach leveraging advanced\nmodels such as BERT and RoBERTa for Message-Level Analysis and a Context\nDetermination approach for classifying actor interactions, including the\nintroduction of Actor Significance Thresholds and Message Significance\nThresholds. The proposed method aims to enhance accuracy and robustness in\ndetecting OG by considering the dynamic and multi-faceted nature of these\nattacks. Cross-dataset experiments evaluate the robustness and versatility of\nour approach. This paper's contributions include improved detection\nmethodologies and the potential for application in various scenarios,\naddressing gaps in current literature and practices.","PeriodicalId":501301,"journal":{"name":"arXiv - CS - Machine Learning","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Machine Learning","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07958","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

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.
利用上下文判断和信息级分析增强在线疏导检测
网络诱拐(OG)是一种普遍存在的威胁,主要是儿童在网络上面临的威胁,诱拐者在社交媒体/信息平台上使用欺骗方法利用儿童的弱点。这些攻击会造成严重的心理和生理影响,包括受害倾向。目前的技术措施还不够完善,尤其是端到端加密技术的发明阻碍了信息监控。现有的解决方案主要集中在虐童媒体的签名分析上,这并不能有效地解决实时 OG 检测问题。本文认为,OG 攻击非常复杂,需要识别成人和儿童之间的特定通信模式。本文介绍了一种利用 BERT 和 RoBERTa 等先进模型进行消息级分析的新方法,以及一种对行为者互动进行分类的上下文确定方法,包括引入行为者重要性阈值和消息重要性阈值。所提出的方法旨在通过考虑这些攻击的动态和多面性,提高检测 OG 的准确性和鲁棒性。跨数据集实验评估了我们方法的鲁棒性和通用性。本文的贡献包括改进了检测方法,并具有在各种场景中应用的潜力,填补了当前文献和实践中的空白。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信