Malicious URL Detection based on Machine Learning
Malicious URL Detection based on Machine Learning
复制标题
基于机器学习的恶意URL检测
DOI：
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复制
发表时间：
2020
影响因子：
0.9
通讯作者：
Tisenko Victor Nikolaevich
中科院分区：
文献类型：
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作者：
Cho Do Xuan;H. Nguyen;Tisenko Victor Nikolaevich
当前，网络信息不安全风险在数量和危害程度上都在迅速增加。如今，黑客主要使用的方法是攻击端到端技术和利用人类漏洞。这些技术包括社会工程、网络钓鱼、域欺骗等。进行这些攻击的步骤之一是使用恶意的统一资源定位符（URL）欺骗用户。因此，恶意URL检测是当今非常感兴趣的。有几项科学研究显示了许多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，我们提出了一个恶意URL检测方法，使用机器学习技术的基础上，我们提出的URL行为和属性。此外，大数据技术也被用来提高检测恶意URL的异常行为的能力。简而言之，所提出的检测系统由一组新的URL特征和行为、机器学习算法和大数据技术组成。实验结果表明，提出的URL属性和行为能够显著提高恶意URL的检测能力。这表明，该系统可以被认为是一个优化的和友好的恶意URL检测的解决方案。
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. Moreover, bigdata technology is also exploited to improve the capability of detection malicious URLs based on abnormal behaviors. In short, the proposed detection system consists of a new set of URLs features and behaviors, a machine learning algorithm, and a bigdata technology. The experimental results show that the proposed URL attributes and behavior can help improve the ability to detect malicious URL significantly. This is suggested that the proposed system may be considered as an optimized and friendly used solution for malicious URL detection.
DOI：
10.1007/3-540-45014-9
发表时间：
2000-06
期刊：
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影响因子：
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作者：
Thomas G. Dietterich
通讯作者：
Thomas G. Dietterich