A distributed architecture for phishing detection using Bayesian Additive Regression Trees

Saeed Abu-Nimeh, D. Nappa, Xinlei Wang, S. Nair
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引用次数: 11

Abstract

With the variety of applications in mobile devices, such devices are no longer deemed calling gadgets merely. Various applications are used to browse the Internet, thus access financial data, and store sensitive personal information. In consequence, mobile devices are exposed to several types of attacks. Specifically, phishing attacks can easily take advantage of the limited or lack of security and defense applications therein. Furthermore, the limited power, storage, and processing capabilities render machine learning techniques inapt to classify phishing and spam emails in such devices. The present study proposes a distributed architecture hinging on machine learning approaches to detect phishing emails in a mobile environment based on a modified version of Bayesian additive regression trees (BART). Apparently, BART suffers from high computational time and memory overhead, therefore, distributed algorithms are proposed to accommodate detection applications in resource constrained wireless environments.
使用贝叶斯加性回归树的分布式网络钓鱼检测架构
随着移动设备应用的多样化,这些设备不再仅仅被认为是打电话的小工具。各种各样的应用程序被用来浏览互联网,从而访问金融数据,并存储敏感的个人信息。因此,移动设备暴露在几种类型的攻击之下。具体来说,网络钓鱼攻击可以很容易地利用其中有限或缺乏安全性和防御的应用程序。此外,有限的功率、存储和处理能力使得机器学习技术无法对此类设备中的网络钓鱼和垃圾邮件进行分类。本研究提出了一种基于机器学习方法的分布式架构,以基于修改版本的贝叶斯加性回归树(BART)来检测移动环境中的网络钓鱼电子邮件。显然,BART具有较高的计算时间和内存开销,因此,提出了分布式算法来适应资源受限的无线环境中的检测应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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