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arXiv 2412.09641cs.CRcs.LG
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面向移动安全的机器学习驱动的Smishing检测框架

Machine Learning Driven Smishing Detection Framework for Mobile Security

CSIRO’s Data61(澳大利亚联邦科学与工业研究组织Data61);National Institute of Technology, Kurukshetra(库鲁克谢特拉国家理工学院);University of Adelaide(阿德莱德大学);PGIMS, Haryana, India(印度哈里亚纳邦PGIMS)

Diksha Goel, Hussain Ahmad, Ankit Kumar Jain, Nikhil Kumar Goel

AI总结:

针对移动环境下传统方法难检测非正式且演变的短信钓鱼问题,提出基于文本归一化的机器学习检测框架,在公开数据集上达96.2%准确率,优于现有方法。

摘要:

人们越来越依赖智能手机进行通信、金融交易和个人数据管理,这使得智能手机成为网络攻击的主要目标,尤其是smishing(短信钓鱼)——一种通过SMS实施的复杂钓鱼变体。尽管威胁日益严峻,传统检测方法往往难以应对SMS语言非正式且不断演变的特性,这类语言包含缩写、俚语和简写形式。本文提出一种增强的基于内容的smishing检测框架,该框架利用先进的文本归一化技术提升检测准确率。通过将非标准文本转换为标准形式,所提模型提升了机器学习分类器(尤其是Naive Bayesian分类器)区分smishing消息与合法消息的效能。我们在公开数据集上验证的实验结果显示,检测准确率达96.2%,误报率低至3.87%,漏报率为2.85%。该方法显著优于现有方法,为移动环境中日益复杂的smishing威胁提供了可靠的解决方案。

英文摘要:

The increasing reliance on smartphones for communication, financial transactions, and personal data management has made them prime targets for cyberattacks, particularly smishing, a sophisticated variant of phishing conducted via SMS. Despite the growing threat, traditional detection methods often struggle with the informal and evolving nature of SMS language, which includes abbreviations, slang, and short forms. This paper presents an enhanced content-based smishing detection framework that leverages advanced text normalization techniques to improve detection accuracy. By converting nonstandard text into its standardized form, the proposed model enhances the efficacy of machine learning classifiers, particularly the Naive Bayesian classifier, in distinguishing smishing messages from legitimate ones. Our experimental results, validated on a publicly available dataset, demonstrate a detection accuracy of 96.2%, with a low False Positive Rate of 3.87% and False Negative Rate of 2.85%. This approach significantly outperforms existing methodologies, providing a robust solution to the increasingly sophisticated threat of smishing in the mobile environment.

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。

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