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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.
Security and Performance Implications of BGP Rerouting-resistant Guard Selection Algorithms for Tor
(2020)
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.
Out-of-the-box Multipath TCP as a Tor Transport Protocol: Performance and Privacy Implications
(2020)
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.
Controller Area Network (CAN) is a masterless serial bus designed and widely used for the exchange of mission and time-critical information within commercial vehicles. In-vehicle communication is based on messages sent and received by Electronic Control Units (ECUs) connected to this serial bus network. Although unencrypted, CAN messages are not easy to interpret. In fact, Original Equipment Manufacturers (OEMs) attempt to achieve security through obscurity by encoding the data in their proprietary format, which is kept secret from the general public. As a result, the only way to obtain clear data is to reverse engineer CAN messages. Driven by the need for in-vehicle message interpretation, which is highly valuable in the automotive industry, researchers and companies have been working to make this process automated, fast, and standardized. In this paper, we provide a comprehensive review of the state of the art and summarize the major advances in CAN bus reverse engineering. We are the first to provide a taxonomy of CAN tokenization and translation techniques. Based on the reviewed literature, we highlight an important issue: the lack of a public and standardized dataset for the quantitative evaluation of translation algorithms. In response, we define a complete set of requirements for standardizing the data collection process. We also investigate the risks associated with the automation of CAN reverse engineering, in particular with respect to the security network and the safety and privacy of drivers and passengers. Finally, we discuss future research directions in CAN reverse engineering.