LookAhead：通过预测建模增强众包网站声誉系统
LookAhead: Augmenting Crowdsourced Website Reputation Systems With Predictive Modeling
摘要：
不安全的网站包括恶意网站和不适当的网站，例如托管可疑或攻击性内容的网站。网站信誉系统旨在帮助普通用户远离这些不安全的网站。然而，为网站分配安全评级的过程通常涉及人类。因此，它是耗时的、昂贵的并且不可扩展的。这导致了两个主要问题：（i）很大一部分网络空间仍然没有评级，（ii）在对新网站评级之前有一个不可接受的时间差。在本文中，我们表明，通过利用网站的结构和基于内容的属性，可以可靠和有效地预测其安全评级，从而减轻这两个问题。我们使用多达90，000个网站的四个数据集来证明我们方法的有效性。我们使用信任网络（WOT）的评级，这是一个流行的众包网络声誉系统，作为基础事实。我们提出了一种新的集成分类技术，利用现有的网页结构和内容属性的机会来预测他们的最终评级在两个方面所使用的WOT：可信度和儿童安全。我们是第一个预测这种主观评级的分类系统，同样的方法在识别恶意网站方面也同样有效。在所有数据集上，我们的分类表现良好，平均F $_1$ -得分在74- 90\ %范围内。
英文摘要：
Unsafe websites consist of malicious as well as inappropriate sites, such as those hosting questionable or offensive content. Website reputation systems are intended to help ordinary users steer away from these unsafe sites. However, the process of assigning safety ratings for websites typically involves humans. Consequently it is time consuming, costly and not scalable. This has resulted in two major problems: (i) a significant proportion of the web space remains unrated and (ii) there is an unacceptable time lag before new websites are rated. In this paper, we show that by leveraging structural and content-based properties of websites, it is possible to reliably and efficiently predict their safety ratings, thereby mitigating both problems. We demonstrate the effectiveness of our approach using four datasets of up to 90,000 websites. We use ratings from Web of Trust (WOT), a popular crowdsourced web reputation system, as ground truth. We propose a novel ensemble classification technique that makes opportunistic use of available structural and content properties of webpages to predict their eventual ratings in two dimensions used by WOT: trustworthiness and child safety. Ours is the first classification system to predict such subjective ratings and the same approach works equally well in identifying malicious websites. Across all datasets, our classification performs well with average F$_1$-score in the 74--90\% range.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
