恶意网站分类的Map Reduce实现

Maminur Islam, Subash Poudyal, Kishor Datta Gupta
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引用次数: 3

摘要

由于互联网的快速发展,恶意网站[1]已经成为网络犯罪活动的基石。现有的检测良性和恶意网站的方法有很多,其中一些方法的准确率接近99%。然而,对恶意网站进行有效、高效的检测,现在从准确性上看已经足够合理,但从处理速度上看,由于其质量和复杂性,仍然被认为是一项巨大而昂贵的任务。在这个项目中,我们想实现一个分类器,它可以使用Kaggle数据集中可用的网络和应用程序特征来检测良性和恶意网站,我们将使用MapReduce来实现这一目标,使分类速度比传统方法更快[2]。
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Map Reduce Implementation for Malicious Websites Classification
Due to the rapid growth of the internet, malicious websites [1] have become the cornerstone for internet crime activities. There are lots of existing approaches to detect benign and malicious websites — some of them giving near 99% accuracy. However, effective and efficient detection of malicious websites has now seemed reasonable enough in terms of accuracy, but in terms of processing speed, it is still considered an enormous and costly task because of their qualities and complexities. In this project, We wanted to implement a classifier that would detect benign and malicious websites using network and application features that are available in a data-set from Kaggle, and we will do that using MapReduce to make the classification speeds faster than the traditional approaches.[2].
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