TY - GEN A1 - De la Cadena, Wladimir A1 - Mitseva, Asya A1 - Hiller, Jens A1 - Pennekamp, Jan A1 - Reuter, Sebastian A1 - Filter, Julian A1 - Engel, Thomas A1 - Wehrle, Klaus A1 - Panchenko, Andriy T1 - TrafficSliver: Fighting Website Fingerprinting Attacks with Traffic Splitting T2 - CCS '20: Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, October 2020 N2 - 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. KW - Traffic Analysis KW - Website Fingerprinting KW - Privacy KW - Anonymous Communication KW - Onion Routing KW - Web Privacy Y1 - 2020 SN - 978-1-4503-7089-9 U6 - https://doi.org/10.1145/3372297.3423351 SP - 1971 EP - 1985 PB - Association for Computing Machinery CY - New York ER - TY - GEN A1 - De la Cadena, Wladimir A1 - Kaiser, Daniel A1 - Panchenko, Andriy A1 - Engel, Thomas T1 - Out-of-the-box Multipath TCP as a Tor Transport Protocol: Performance and Privacy Implications T2 - 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA), 24-27 Nov. 2020, Cambridge, MA, USA Y1 - 2020 SN - 978-1-7281-8326-8 SN - 978-1-7281-8327-5 U6 - https://doi.org/10.1109/NCA51143.2020.9306702 SN - 2643-7929 ER - TY - GEN A1 - Buscemi, Alessio A1 - Turcanu, Ion A1 - Castignani, German A1 - Panchenko, Andriy A1 - Engel, Thomas A1 - Shin, Kang G. T1 - A Survey on Controller Area Network Reverse Engineering T2 - IEEE Communications Surveys & Tutorials N2 - 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. KW - Can bus KW - reverse engineering KW - security KW - connected Vechiles Y1 - 2023 UR - https://ieeexplore.ieee.org/abstract/document/10092880 U6 - https://doi.org/10.1109/COMST.2023.3264928 SN - 1553-877X VL - 25 IS - 3,3 SP - 1445 EP - 1481 PB - IEEE ER -