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Institute
Users of an onion routing network, such as Tor, depend on its anonymity properties. However, especially malicious entry nodes, which know the client’s identity, can also observe the whole communication on their link to the client and, thus, conduct several de-anonymization attacks. To limit this exposure and to impede corresponding attacks, we propose to multipath traffic between the client and the middle node to reduce the information an attacker can obtain at a single vantage point. To facilitate the deployment, only clients and selected middle nodes need to implement our approach, which works transparently for the remaining legacy nodes. Furthermore, we let clients control the splitting strategy to prevent any external manipulation.
Public Key Infrastructures (PKIs) with their trusted Certificate Authorities (CAs) provide the trust backbone for the Internet: CAs sign certificates which prove the identity of servers, applications, or users. To be trusted by operating systems and browsers, a CA has to undergo lengthy and costly validation processes. Alternatively, trusted CAs can cross-sign other CAs to extend their trust to them. In this paper, we systematically analyze the present and past state of cross-signing in the Web PKI. Our dataset (derived from passive TLS monitors and public CT logs) encompasses more than 7 years and 225 million certificates with 9.3 billion trust paths. We show benefits and risks of cross-signing. We discuss the difficulty of revoking trusted CA certificates where, worrisome, cross-signing can result in valid trust paths to remain after revocation; a problem for non-browser software that often blindly trusts all CA certificates and ignores revocations. However, cross-signing also enables fast bootstrapping of new CAs, e.g., Let's Encrypt, and achieves a non-disruptive user experience by providing backward compatibility. In this paper, we propose new rules and guidance for cross-signing to preserve its positive potential while mitigating its risks.
Website fingerprinting (WFP) aims to infer information about the
content of encrypted and anonymized connections by observing
patterns of data flows based on the size and direction of packets. By
collecting traffic traces at a malicious Tor entry node — one of the
weakest adversaries in the attacker model of Tor — a passive eavesdropper can leverage the captured meta-data to reveal the websites visited by a Tor user. As recently shown, WFP is significantly more effective and realistic than assumed. Concurrently, former WFP defenses are either infeasible for deployment in real-world settings or defend against specific WFP attacks only.
To limit the exposure of Tor users to WFP, we propose novel
lightweight WFP defenses, TrafficSliver, which successfully counter
today’s WFP classifiers with reasonable bandwidth and latency
overheads and, thus, make them attractive candidates for adoption
in Tor. Through user-controlled splitting of traffic over multiple
Tor entry nodes, TrafficSliver limits the data a single entry node
can observe and distorts repeatable traffic patterns exploited by
WFP attacks.We first propose a network-layer defense, in which we
apply the concept of multipathing entirely within the Tor network.
We show that our network-layer defense reduces the accuracy from
more than 98% to less than 16% for all state-of-the-art WFP attacks
without adding any artificial delays or dummy traffic. We further
suggest an elegant client-side application-layer defense, which is
independent of the underlying anonymization network. By sending
single HTTP requests for different web objects over distinct Tor
entry nodes, our application-layer defense reduces the detection
rate of WFP classifiers by almost 50 percentage points. Although it
offers lower protection than our network-layer defense, it provides
a security boost at the cost of a very low implementation overhead and is fully compatible with today's Tor network.
In recent years, the amount of traffic protected
with Transport Layer Security (TLS) has significantly increased
and new protocols such as HTTP/2 and QUIC further foster
this emerging trend. However, protecting traffic with TLS has
significant impacts on network entities. While the restrictions for
middleboxes have been extensively studied, addressing the impact
of TLS on clients and servers has been mostly neglected so far.
Especially mobile clients in emerging 5G and IoT deployments
suffer from significantly increased latency, traffic, and energy
overheads when protecting traffic with TLS. In this paper,
we address this emerging topic by thoroughly analyzing the
impact of TLS on clients and servers and derive opportunities
for significantly decreasing latency of TLS communication and
downsizing TLS management traffic, thereby also reducing TLSinduced
server load. We propose a protocol compatible redesign
of TLS session management to use these opportunities and
showcase their potential based on mobile device traffic and mobile
web-browsing traces. These show promising potentials for latency
improvements by up to 25.8% and energy savings of up to 26.3%.
Website fingerprinting (WFP) attacks on the anonymity network Tor have become ever more effective. Furthermore, research discovered that proposed defenses are insufficient or cause high overhead. In previous work, we presented a new WFP defense for Tor that incorporates multipath transmissions to repel malicious Tor nodes from conducting WFP attacks. In this demo, we showcase the operation of our traffic splitting defense by visually illustrating the underlying Tor multipath transmission using LED-equipped Raspberry Pis.
Tailoring Onion Routing to the Internet of Things: Security and Privacy in Untrusted Environments
(2019)
An increasing number of IoT scenarios involve mobile, resource-constrained IoT devices that rely on untrusted networks for Internet connectivity. In such environments, attackers can derive sensitive private information of IoT device owners, e.g., daily routines or secret supply chain procedures, when sniffing on IoT communication and linking IoT devices and owner. Furthermore, untrusted networks do not provide IoT devices with any protection against attacks from the Internet. Anonymous communication using onion routing provides a well-proven mechanism to keep the relationship between communication partners secret and (optionally) protect against network attacks. However, the application of onion routing is challenged by protocol incompatibilities and demanding cryptographic processing on constrained IoT devices, rendering its use infeasible. To close this gap, we tailor onion routing to the IoT by bridging protocol incompatibilities and offloading expensive cryptographic processing to a router or web server of the IoT device owner. Thus, we realize resource-conserving access control and end-toend security for IoT devices. To prove applicability, we deploy onion routing for the IoT within the well-established Tor network enabling IoT devices to leverage its resources to achieve the same grade of anonymity as readily available to traditional devices.
Website fingerprinting (WFP) is a special type of traffic analysis, which aims to infer the websites visited by a user. Recent studies have shown that WFP targeting Tor users is notably more effective than previously expected. Concurrently, state-of-the-art defenses have been proven to be less effective. In response, we present a novel WFP defense that splits traffic over multiple entry nodes to limit the data a single malicious entry can use. Here, we explore several traffic-splitting strategies to distribute user traffic. We establish that our weighted random strategy dramatically reduces the accuracy from nearly 95% to less than 35% for four state-of-the-art WFP attacks without adding any artificial delays or dummy traffic.