Website fingerprinting attacks enable an adversary to infer which website a victim is visiting, even if the victim uses an encrypting proxy, such as Tor. Previous work has shown that all proposed defenses against website fingerprinting attacks are ineffective. This paper advances the study of website fingerprinting attacks and defenses in two ways. First, we develop bounds on the trade-off between security and bandwidth overhead that any fingerprinting defense scheme can achieve. This enables us to compare schemes with different security overhead trade-offs by comparing how close they are to the lower bound. We then refine, implement, and evaluate the Congestion Sensitive BuFLO scheme outlined by Cai, et al CS-BuFLO, which is based on the provably-secure BuFLO defense proposed by Dyer, et al, was not fully-specified by Cai, et al, but has nonetheless attracted the attention of the Tor developers. Our experiments find that CS-BuFLO has high overhead (around 2.3-2.8x) but can get 6x closer to the bandwidth security trade-off lower bound than Tor or plain SSH.
Application-level defenses alter the sequence of HTTP requests and responses to further obfuscate the user's activity. For example, HTTPOS uses HTTP pipelining, HTTP Range requests, dummy requests, extraneous HTTP headers, multiple TCP connections, and munges TCP window sizes and maximum segment size (MSS) fields @cite_9 . Tor has also released an experimental version of Firefox that randomizes the order in which embedded objects are requested, and the level of pipelining used by the browser during the requests @cite_18 . Both schemes were defeated by Cai, et al @cite_13 .
@cite_13 @cite_9 @cite_18
2396066572 2170085959 2164686665 2159160833
Leakage of private information from web applications— even when the traffic is encrypted—is a major security threat to many applications that use HTTP for data delivery. This paper considers the problem of inferring from encrypted HTTP traffic the web sites or web pages visited by a user. Existing browser-side approaches to this problem cannot defend against more advanced attacks, and serverside approaches usually require modifications to web entities, such as browsers, servers, or web objects. In this paper, we propose a novel browser-side system, namely HTTPOS, to prevent information leaks and offer much better scalability and flexibility. HTTPOS provides a comprehensive and configurable suite of traffic transformation techniques for a browser to defeat traffic analysis without requiring any server-side modifications. Extensive evaluation of HTTPOS on live web traffic shows that it can successfully prevent the state-of-the-art attacks from inferring private information from encrypted HTTP flows. We present a novel web page fingerprinting attack that is able to defeat several recently proposed defenses against traffic analysis attacks, including the application-level defenses HTTPOS and randomized pipelining over Tor. Regardless of the defense scheme, our attack was able to guess which of 100 web pages a victim was visiting at least 50 of the time and, with some defenses, over 90 of the time. Our attack is based on a simple model of network behavior and out-performs previously proposed ad hoc attacks. We then build a web site fingerprinting attack that is able to identify whether a victim is visiting a particular web site with over 90 accuracy in our experiments. Our results strongly suggest that ad hoc defenses against traffic analysis are not likely to succeed. Consequently, we describe a defense scheme that provides provable security properties, albeit with potentially higher overheads. Attackers can render distributed denial-of-service attacks more difficult to defend against by bouncing their flooding traffic off of reflectors; that is, by spoofing requests from the victim to a large set of Internet servers that will in turn send their combined replies to the victim. The resulting dilution of locality in the flooding stream complicates the victim's abilities both to isolate the attack traffic in order to block it, and to use traceback techniques for locating the source of streams of packets with spoofed source addresses, such as ITRACE [Be00a], probabilistic packet marking [SWKA00], [SP01], and SPIE [S+01]. We discuss a number of possible defenses against reflector attacks, finding that most prove impractical, and then assess the degree to which different forms of reflector traffic will have characteristic signatures that the victim can use to identify and filter out the attack traffic. Our analysis indicates that three types of reflectors pose particularly significant threats: DNS and Gnutella servers, and TCP-based servers (particularly Web servers) running on TCP implementations that suffer from predictable initial sequence numbers. We argue in conclusion in support of "reverse ITRACE" [Ba00] and for the utility of packet traceback techniques that work even for low volume flows, such as SPIE. Launching a denial of service (DoS) attack is trivial, but detection and response is a painfully slow and often a manual process. Automatic classification of attacks as single- or multi-source can help focus a response, but current packet-header-based approaches are susceptible to spoofing. This paper introduces a framework for classifying DoS attacks based on header content, and novel techniques such as transient ramp-up behavior and spectral analysis. Although headers are easily forged, we show that characteristics of attack ramp-up and attack spectrum are more difficult to spoof. To evaluate our framework we monitored access links of a regional ISP detecting 80 live attacks. Header analysis identified the number of attackers in 67 attacks, while the remaining 13 attacks were classified based on ramp-up and spectral analysis. We validate our results through monitoring at a second site, controlled experiments, and simulation. We use experiments and simulation to understand the underlying reasons for the characteristics observed. In addition to helping understand attack dynamics, classification mechanisms such as ours are important for the development of realistic models of DoS traffic, can be packaged as an automated tool to aid in rapid response to attacks, and can also be used to estimate the level of DoS activity on the Internet.