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:  The Encrypted Data Transport Phase 
1. Abstract of Cited Paper (BIB001):  SSH attacks are a main area of concern for network managers, due to the danger associated with a successful compromise. Detecting these attacks, and possibly compromised victims, is therefore a crucial activity. Most existing network intrusion detection systems designed for this purpose rely on the inspection of individual packets and, hence, do not scale to today's high-speed networks. To overcome this issue, this paper proposes SSHCure, a flow-based intrusion detection system for SSH attacks. It employs an efficient algorithm for the real-time detection of ongoing attacks and allows identification of compromised attack targets. A prototype implementation of the algorithm, including a graphical user interface, is implemented as a plugin for the popular NfSen monitoring tool. Finally, the detection performance of the system is validated with empirical traffic data. 
2. Abstract of Cited Paper (BIB002):  Many Transport Layer Security (TLS) servers use the ephemeral Diffie-Hellman (DHE) key exchange to support forward secrecy. However, in a survey of 473,802 TLS servers, the authors found that 82.9 percent of the DHE-enabled servers use weak DH parameters, resulting in a false sense of security. They compared the server throughput of various TLS setups, and measured real-world client-side latencies using an advertisement network. Their results indicate that using forward secrecy is no harder, and can even be faster using elliptic curve cryptography (ECC), than no forward secrecy. 
3. Abstract of Cited Paper (BIB003):  Revelations of large scale electronic surveillance and data mining by governments and corporations have fueled increased adoption of HTTPS. We present a traffic analysis attack against over 6000 webpages spanning the HTTPS deployments of 10 widely used, industry-leading websites in areas such as healthcare, finance, legal services and streaming video. Our attack identifies individual pages in the same website with 89% accuracy, exposing personal details including medical conditions, financial and legal affairs and sexual orientation. We examine evaluation methodology and reveal accuracy variations as large as 18% caused by assumptions affecting caching and cookies. We present a novel defense reducing attack accuracy to 27% with a 9% traffic increase, and demonstrate significantly increased effectiveness of prior defenses in our evaluation context, inclusive of enabled caching, user-specific cookies and pages within the same website.

Literature Review:
Information about network traffic can be extracted from encrypted data which is transported between communicating parties. Packets exchanged during the transport phase usually contain only information about the packet itself, such as the length and the authentication field which are not useful for monitoring network traffic. Nevertheless, two methods do exists to obtain more suitable data. The first method uses direct traffic decryption, which is possible to perform only if the shared secret of the connection is known. Therefore, decryption can be used in networks on the servers' side where organizations know the private key of the connected server. However, such decryption would be impossible if algorithms wee used which ensure forward secrecy BIB003 . The second method is based on the extraction of traffic features. An example of such an analysis is presented by Miller et al. BIB004 who monitor the size of TLS encrypted packet sequences. Based on their data, together with various predictive models, they are able to identify a specific web page and deduce its content even though the traffic is encrypted. A similar approach was also used by Koch and Rodosek BIB001 for analysing SSH traffic. Another example of using traffic features is the work by Hellemons et al. BIB002 , which focused on intrusion detection in SSH traffic. Encrypted traffic features could also be used for classifying encrypted traffic, which is described in Section 6.