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.

Micro Aerial Vehicle (MAV)-assisted localization of wireless nodes in a given area is especially important in post-disaster scenarios where no other communication infrastructure is accessibleDifferent from related work solely focusing on simulations, we have done a measurement campaign using a multirotor MAV to derive a realistic propagation model for our scenario. Based on this propagation model and a novel positioning approach, the performance of different trajectories is reevaluatedOur results show that in practice, the variance of the measurements is higher, than commonly expected in related work on MAV-assisted localization. As a result, the study of our developed trajectories show localization errors of 6-7m in a 400x400m2area. We also show that adaptive trajectories outperform state-of-the-art algorithms and are able to localize at least 90% of the nodes in under 20 minutes. Thus, they also outperform other algorithms with respect to the effectiveness of the trajectory.

Nowadays, Micro Aerial Vehicles (MAVs) find applications in different areas, such as search and rescue, photography, inspection, surveillance, etc. The MAVs, with weight up to several kilograms, are promising candidates for rescue operations during disasters such as earthquakes, floods and hurricanes. Equipping those with radio interfaces will allow to re-establish radio communication in disaster scenario, or be used for localization of survived victims. However, equipping MAV with helpful devices is limited by payload and battery capacitance. In this work, we present a novel flexible Software-defined radio enabled micro AerIal vehicLe (SkySAIL), as a powerful platform to support different RF sensing applications. The SkySAIL is a multifunctional multi-rotor MAV equipped with a frequency agile multichannel software-defined radio (SDR). In contrast to the state-of-the-art, our solution can flexibly support multiple communication technologies depending on application scenario, remaining a very effective (in terms of energy consumption and localization accuracy), robust and mobile approach. Results indicate that SkySAIL autonomously localizes GSM mobile stations (MSs) more accurately than state-of-the-art approaches with a mean relative localization error of 13% (3.22 m in absolute measures).

The main focus of this work is on the exploration of static trajectories. A new Optimized Route Planning for the Localization of Indoor Nodes from Outdoor eNvironment (OrLiOn) is introduced. Three building-aware trajectories, specifically Extended-Hilbert, Triangle and Z-trajectory are derived with the help of the introduced algorithm. Further, these trajectories are simulated and analyzed in terms of the localization error and difficulty. The obtained results indicate the superiority of the Extended-Hilbert and Triangle trajectories in terms of localization accuracy, when compared to the Z-trajectory, Circle and Octagon trajectories.