Malicious URL Detection and Identification
Malicious URL Detection and Identification
复制标题
恶意URL检测与识别
DOI：
--
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
A. Dixit
中科院分区：
文献类型：
--
作者：
Anjali B. Sayamber;A. Dixit
链接被分发频道用作来源，以在整个网络上广播恶意软件。这些链接有助于对攻击者进行部分或完整的系统控制。这导致受害者系统很容易被感染，并且攻击者可以将系统用于各种网络犯罪，例如窃取证书，垃圾邮件，网络钓鱼，服务拒绝和更多此类攻击。检测此类犯罪系统应该快速准确地发现新的恶意内容。本文介绍了与URL（统一资源定位器）分类过程相关的各个方面，该过程识别目标网站是恶意的还是良性的。标准数据集用于来自不同来源的培训目的。不断上升的问题垃圾邮件，网络钓鱼和恶意软件产生了对可靠的框架解决方案的需求，该解决方案可以分类并进一步识别恶意URL。已经提出了一种替代方法，该方法使用天真的贝叶斯分类器进行自动分类和检测恶意URL。基于天真的贝叶斯的建议模型得到了聚类和分类技术的支持。另一方面，它们很少用于一般的概率学习和推理，通常用于通过条件和边缘分布进行估计。本文提出的工作表明，对于广泛的基准数据集，使用概率模型学到的天真贝叶斯模型比支持向量机模型具有更好的精度。
links are used as a source by the distribution channels to broadcast malware all over the Web. These links become instrumental in giving partial or full system control to the attackers. This results in victim systems, which get easily infected and, attackers can utilize systems for various cyber crimes such as stealing credentials, spamming, phishing, denial-of-service and many more such attacks. To detect such crimes systems should be fast and precise with the ability to detect new malicious content. This paper introduces various aspects associated with the URL (Uniform Resource Locator) classification process which recognizes whether the target website is a malicious or benign. The standard datasets are used for training purpose from different sources. The rising problem spamming, phishing and malware, has generated a need for reliable framework solution which can classify and further identify the malicious URL. An alternative approach has been proposed which uses a Naive Bayes classifier for an automated classification and detection of malicious URLs. The proposed model based on Naive Bayes is supported by clustering and classification technique. On the other hand, they are rarely used for general probabilistic learning and inference which is typically used for estimating with conditional and marginal distributions. The proposed work in this paper shows that, for a wide range of benchmark datasets, Naive Bayes models learned using Probability model has better accuracy than Support Vector Machine model.