TY - GEN A1 - Wichtlhuber, Matthias A1 - Strehle, Eric A1 - Kopp, Daniel A1 - Prepens, Lars A1 - Stegmueller, Stefan A1 - Rubina, Alina A1 - Dietzel, Christoph A1 - Hohlfeld, Oliver T1 - IXP Scrubber: Learning from Blackholing Traffic for ML-Driven DDoS Detection at Scale T2 - ACM SIGCOMM 2022 Conference, 2022 N2 - Distributed Denial of Service (DDoS) attacks are among the most critical cybersecurity threats, jeopardizing the stability of even the largest networks and services. The existing range of mitigation services predominantly filters at the edge of the Internet, thus creating unnecessary burden for network infrastructures. Consequently, we present IXP Scrubber, a Machine Learning (ML) based system for detecting and filtering DDoS traffic at the core of the Internet at Internet Exchange Points (IXPs) which see large volumes and varieties of DDoS. IXP Scrubber continuously learns DDoS traffic properties from neighboring Autonomous Systems (ASes). It utilizes BGP signals to drop traffic for certain routes (blackholing) to sample DDoS and can thus learn new attack vectors without the operator’s intervention and on unprecedented amounts of training data. We present three major contributions: i) a method to semi-automatically generate arbitrarily large amounts of labeled DDoS training data from IXPs’ sampled packet traces, ii) the novel, controllable, locally explainable and highly precise two-step IXP Scrubber ML model, and iii) an evaluation of the IXP Scrubber ML model, including its temporal and geographical drift, based on data from 5 IXPs covering a time span of up to two years. KW - Machine Learning, Traffic Classification, Denial of Service Y1 - 2022 UR - https://dl.acm.org/doi/pdf/10.1145/3544216.3544268 SP - 707 EP - 722 ER - TY - GEN A1 - Moreno, José Miguel A1 - Pastrana, Sergio A1 - Reelfs, Jens Helge A1 - Vallina, Pelayo A1 - Panchenko, Andriy A1 - Smaragdakis, Georgios A1 - Hohlfeld, Oliver A1 - Vallina-Rodriguez, Narseo A1 - Tapiador, Juan T1 - Reviewing War: Unconventional User Reviews as a Side Channel to Circumvent Information Controls N2 - During the first days of the 2022 Russian invasion of Ukraine, Russia’s media regulator blocked access to many global social media platforms and news sites, including Twitter, Facebook, and the BBC. To bypass the information controls set by Russian authorities, pro-Ukrainian groups explored unconventional ways to reach out to the Russian population, such as posting war-related content in the user reviews of Russian business available on Google Maps or Tripadvisor. This paper provides a first analysis of this new phenomenon by analyzing the creative strategies to avoid state censorship. Specifically, we analyze reviews posted on these platforms from the beginning of the conflict to September 2022. We measure the channeling of war messages through user reviews in Tripadvisor and Google Maps, as well as in VK, a popular Russian social network. Our analysis of the content posted on these services reveals that users leveraged these platforms to seek and exchange humanitarian and travel advice, but also to disseminate disinformation and polarized messages. Finally, we analyze the response of platforms in terms of content moderation and their impact. KW - Side Channels KW - Disinformation KW - Propaganda KW - User-Generated Content KW - Russia KW - Ukraine KW - Tripadvisor KW - Google Maps KW - VKontakte Y1 - 2023 UR - https://arxiv.org/abs/2302.00598 U6 - https://doi.org/10.48550/arXiv.2302.00598 ER - TY - GEN A1 - Schweissguth, Eike A1 - Parzyjegla, Helge A1 - Danielis, Peter A1 - Mühl, Gero A1 - Timmermann, Dirk A1 - Mehner, Stefan A1 - Hohlfeld, Oliver A1 - Hellmanns, David A1 - Falk, Jonathan T1 - TSN Scheduler Benchmarking T2 - 2023 IEEE 19th International Conference on Factory Communication Systems (WFCS) N2 - Time-Sensitive Networking (TSN) disrupts realtime communication technology by making IEEE Ethernet realtime-capable. For time-triggered, hard realtime traffic, TSN provides standardized mechanisms to reserve communication paths as well as individual transmission time slots for data frames. By leveraging these means in a precomputed network schedule, TSN allows for bounded end-to-end delays and minimal jitter. Not being part of the IEEE standard, corresponding scheduling algorithms are an active field of research. Unfortunately, due to differing model assumptions, evaluation setups, and key metrics, a fair comparison of schedulers is impossible so far. In this paper, we present a systematic and reproducible approach to benchmark TSN schedulers. First, we provide a scheduler taxonomy that enables to cluster schedulers by their characteristics. Second, we analyze interactions of input parameters and scheduler results to derive a benchmarking parcour for quantitative comparisons. Finally, we use the approach to benchmark existing schedulers and show subtle interaction effects. This way, our approach enables—for the first time-comparability between schedulers, fueled by the public availability of our benchmarking scenarios. KW - Schedules KW - Systematics KW - Scheduling algorithms KW - Taxonomy KW - Process control KW - Standardization KW - Benchmark testing Y1 - 2023 UR - https://ieeexplore.ieee.org/document/10144227/authors#full-text-header SN - 978-1-6654-6432-1 SN - 978-1-6654-6433-8 U6 - https://doi.org/10.1109/WFCS57264.2023.10144227 SN - 2835-8414 SN - 2835-8511 PB - IEEE ER -