我知道你最后一分钟看到了什么-加密的HTTP自适应视频流标题分类
I Know What You Saw Last Minute - Encrypted HTTP Adaptive Video Streaming Title Classification
摘要：
桌面和笔记本电脑可能被恶意利用来侵犯隐私。有两种主要类型的攻击场景：主动和被动。在本文中，我们考虑被动的情况下，对手不主动与设备进行交互，但他能够窃听网络流量的设备从网络侧。大多数互联网流量都是加密的，因此被动攻击具有挑战性。先前的研究表明，信息可以从加密的多媒体流中提取。这包括非HTTP自适应流（非HAS）的视频标题分类。提出了一种加密HTTP自适应视频流标题分类算法。我们表明，外部攻击者可以从视频HTTP自适应流（HAS）网站，如YouTube的视频标题。据我们所知，这是第一个表明这一点的作品。我们提供了一个大的数据集10000 YouTube视频流的100个流行的视频标题（每个标题下载100次）作为这个任务的例子。该数据集是在真实网络条件下收集的。我们提出了几种机器算法的任务，并通过一组实验，这表明我们的分类准确率超过95%。我们还表明，我们的算法能够将不在训练集中的视频标题分类为未知，并且一些算法还能够消除对视频标题的错误预测，而是报告未知。最后，我们评估我们的算法的鲁棒性延迟和数据包丢失在测试时，并表明，使用SVM的解决方案是最强大的对这些变化给予足够的训练数据。我们提供了数据集和爬虫为未来的研究。
英文摘要：
Desktops and laptops can be maliciously exploited to violate privacy. There are two main types of attack scenarios: active and passive. In this paper, we consider the passive scenario where the adversary does not interact actively with the device, but he is able to eavesdrop on the network traffic of the device from the network side. Most of the Internet traffic is encrypted and thus passive attacks are challenging. Previous research has shown that information can be extracted from encrypted multimedia streams. This includes video title classification of non HTTP adaptive streams (non-HAS). This paper presents an algorithm for encrypted HTTP adaptive video streaming title classification. We show that an external attacker can identify the video title from video HTTP adaptive streams (HAS) sites such as YouTube. To the best of our knowledge, this is the first work that shows this. We provide a large data set of 10000 YouTube video streams of 100 popular video titles (each title downloaded 100 times) as examples for this task. The dataset was collected under real-world network conditions. We present several machine algorithms for the task and run a through set of experiments, which shows that our classification accuracy is more than 95%. We also show that our algorithms are able to classify video titles that are not in the training set as unknown and some of the algorithms are also able to eliminate false prediction of video titles and instead report unknown. Finally, we evaluate our algorithms robustness to delays and packet losses at test time and show that a solution that uses SVM is the most robust against these changes given enough training data. We provide the dataset and the crawler for future research.
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。
