You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Survey of Methods for Encrypted Traffic Classification andAnalysis 
Chapter Title:  A Comparison of Classification Tools 
1. Abstract of Cited Paper (BIB001):  Deep Packet Inspection (DPI) is the state-of-the-art technology for traffic classification. According to the conventional wisdom, DPI is the most accurate classification technique. Consequently, most popular products, either commercial or open-source, rely on some sort of DPI for traffic classification. However, the actual performance of DPI is still unclear to the research community, since the lack of public datasets prevent the comparison and reproducibility of their results. This paper presents a comprehensive comparison of 6 well-known DPI tools, which are commonly used in the traffic classification literature. Our study includes 2 commercial products (PACE and NBAR) and 4 open-source tools (OpenDPI, L7-filter, nDPI, and Libprotoident). We studied their performance in various scenarios (including packet and flow truncation) and at different classification levels (application protocol, application and web service). We carefully built a labeled dataset with more than 750K flows, which contains traffic from popular applications. We used the Volunteer-Based System (VBS), developed at Aalborg University, to guarantee the correct labeling of the dataset. We released this dataset, including full packet payloads, to the research community. We believe this dataset could become a common benchmark for the comparison and validation of network traffic classifiers. Our results present PACE, a commercial tool, as the most accurate solution. Surprisingly, we find that some open-source tools, such as nDPI and Libprotoident, also achieve very high accuracy.

Literature Review:
A comparison of the presented open-source tools was introduced by Finsterbusch et al. BIB001 . They prepared a data set containing the traffic of 14 different network protocols such as DNS, HTTP, BitTorrent, and SMTP(S) to compare the tools. Using this data set, they measured classification accuracy, memory usage, CPU utilization, and the number of packets required for proper classification. The comparison showed, amongst other results, that nDPI is not able to classify the BitTorrent protocol with more than a 43 % true positive rate, although it detects SSL/TLS with 100 % accuracy. The Libprotoident tool had the highest classification accuracy of the whole analysed traffic, which was able to classify DNS, HTTP, SIP and e-mail protocols with 100 % accuracy. Libprotoident also needs the least number of packets on average for classifying real-time traffic. Based on the comparison by Finsterbusch et al., we can say that Libprotoident is the most appropriate tool for classifying payload-based traffic, although it is more CPU intensive than the other tools. Another comparison of the tools was carried out by Bujlow et al. BIB002 . They compared all of the previously presented tools. They prepared a publicly available data set for the comparison which contained encrypted and unencrypted network traffic from 17 application protocols, 25 network applications and 34 web services. Their results show that PACE and Libprotoident are the most accurate tools. Nevertheless, the Libprotoident tool was the only classifier able to identify all the encrypted protocols which were tested. Their results were generally very similar to those in the comparison by Finsterbusch et al. In general, the payload-based classification tools use regular expression matching algorithms to identify the encrypted traffic. The main difference between the tools is how much data they need to examine and whether they need both directions of the connection for the classification.