利用NLP技术检测来自URL的网络钓鱼攻击

Ebubekir Buber, B. Diri, O. K. Sahingoz
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引用次数: 32

摘要

如今,网络攻击影响了许多机构和个人,并给他们造成了严重的经济损失。网络钓鱼攻击是最常见的网络攻击类型之一,其目的是利用人们的弱点来获取有关他们的机密信息。这种类型的网络攻击几乎威胁到所有的互联网用户和机构。为了减少这种类型的攻击造成的经济损失,需要用户意识到这一点,并且需要能够检测到它们的应用程序。在2016年最后一个季度,在45个国家的网络钓鱼攻击分析报告中,土耳其的影响率约为43%,仅次于中国。在本研究中,首先解释了这类攻击的特征,然后提出了一种基于机器学习的检测系统。在该系统中,使用自然语言处理技术提取了一些特征。该系统是通过在打开网络钓鱼攻击之前使用一些提取的特征来检查url来实现的。对所创建的系统进行了多次测试,可以看出,在测试的算法中,最佳算法是随机森林算法,成功率为89.9%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detecting phishing attacks from URL by using NLP techniques
Nowadays, cyber attacks affect many institutions and individuals, and they result in a serious financial loss for them. Phishing Attack is one of the most common types of cyber attacks which is aimed at exploiting people's weaknesses to obtain confidential information about them. This type of cyber attack threats almost all internet users and institutions. To reduce the financial loss caused by this type of attacks, there is a need for awareness of the users as well as applications with the ability to detect them. In the last quarter of 2016, Turkey appears to be second behind China with an impact rate of approximately 43% in the Phishing Attack Analysis report between 45 countries. In this study, firstly, the characteristics of this type of attack are explained, and then a machine learning based system is proposed to detect them. In the proposed system, some features were extracted by using Natural Language Processing (NLP) techniques. The system was implemented by examining URLs used in Phishing Attacks before opening them with using some extracted features. Many tests have been applied to the created system, and it is seen that the best algorithm among the tested ones is the Random Forest algorithm with a success rate of 89.9%.
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