A Survey of Methods for Encrypted Traffic Classification andAnalysis <s> The Encrypted Data Transport Phase <s> Intrusion Detection Systems (IDS) are integral components for the detection of malicious code and attacks. Detection methods can be differentiated in signature-based and anomaly-based systems. While the former ones search for well-known patterns which are available in a database, the latter ones build a model of the normal behavior of a network and later on attacks can be detected by measuring significant deviation of the network status against the normal behavior described by the model. Often this requires the availability of the payload of the network packets. If encryption protocols like SSL or SSH are used, searching for attack signatures in the payload is not possible any longer and also the usage of behavior based techniques is limited: Statistical methods like flow evaluation can be used for anomaly detection, but application level attacks hidden in the encrypted traffic can be undetectable. At the moment, only a few systems are designed to cope with encrypted network traffic. Even so, none of these systems can be easily deployed in general because of the need for protocol modifications, special infrastructures or because of high false alarm rates which are not acceptable in a production environment. In this paper, we propose a new IDS for encrypted traffic which identifies command sequences in encrypted network traffic and evaluates the attack possibility of them. The encrypted traffic is clustered and possibilities for different commands are calculated. Based on that, command sequences are analysed. The system evaluates probabilities for commands and command sequences and the likeliness for an attack based on the identified sequences without a decryption of the packets. Because of only using statistical data gathered from the network traffic, the system can be deployed in general. The current prototype of the system focuses on the command evaluation. <s> BIB001 </s> A Survey of Methods for Encrypted Traffic Classification andAnalysis <s> The Encrypted Data Transport Phase <s> 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. <s> BIB002 </s> A Survey of Methods for Encrypted Traffic Classification andAnalysis <s> The Encrypted Data Transport Phase <s> 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. <s> BIB003 </s> A Survey of Methods for Encrypted Traffic Classification andAnalysis <s> The Encrypted Data Transport Phase <s> 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. <s> BIB004
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.