Can web pages be classified using anonymized TCP/IP headers?

Web page classification is useful in many domains- including ad targeting, traffic modeling, and intrusion detection. In this paper, we investigate whether learning-based techniques can be used to classify web pages based only on anonymized TCP/IP headers of traffic generated when a web page is visited. We do this in three steps. First, we select informative TCP/IP features for a given downloaded web page, and study which of these remain stable over time and are also consistent across client browser platforms. Second, we use the selected features to evaluate four different labeling schemes and learning-based classification methods for web page classification. Lastly, we empirically study the effectiveness of the classification methods for real-world applications.