使用机器学习的恶意URL检测

Koteswara Rao Velpula, Kataru Gayathri Priya, Kushwanth Kumar Jammula, Krishna Sruthi Velaga, Praveen Kumar Kongara
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引用次数: 16

摘要

当前,网络信息不安全风险的数量和危险程度都在迅速增加。如今,黑客主要使用的方法是攻击端到端技术并利用人类的弱点。这些技术包括社会工程、网络钓鱼、钓鱼等。实施这些攻击的步骤之一是用恶意的统一资源定位器(url)欺骗用户。因此,恶意URL检测成为当前研究的热点。已经有一些科学研究展示了一些基于机器学习和深度学习技术检测恶意url的方法。在本文中,我们基于我们提出的URL行为和属性,提出了一种使用机器学习技术的恶意URL检测方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Malicious URL Detection Using Machine Learning
Currently, the risk of network information insecurity is increasing rapidly in number and level of danger. The methods mostly used by hackers today is to attack end-to-end technology and exploit human vulnerabilities. These techniques include social engineering, phishing, pharming, etc. One of the steps in conducting these attacks is to deceive users with malicious Uniform Resource Locators (URLs). As a results, malicious URL detection is of great interest nowadays. There have been several scientific studies showing a number of methods to detect malicious URLs based on machine learning and deep learning techniques. In this paper, we propose a malicious URL detection method using machine learning techniques based on our proposed URL behaviors and attributes.
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