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Recent years have seen extensive adoption of domain generation algorithms (DGA) by modern botnets. The main goal is to generate a large number of domain names and then use a small subset for actual C&C communication. This makes DGAs very compelling for botmasters to harden the infrastructure of their botnets and make it resilient to blacklisting and attacks such as takedown efforts. While early DGAs were used as a backup communication mechanism, several new botnets use them as their primary communication method, making it extremely important to study DGAs in detail.
In this paper, we perform a comprehensive measurement study of the DGA landscape by analyzing 43 DGAbased malware families and variants. We also present a taxonomy for DGAs and use it to characterize and compare the properties of the studied families. By reimplementing the algorithms, we pre-compute all possible domains they generate, covering the majority of known and active DGAs. Then, we study the registration status of over 18 million DGA domains and show that corresponding malware families and related campaigns can be reliably identified by pre-computing future DGA domains. We also give insights into botmasters’ strategies regarding domain registration and identify several pitfalls in previous takedown efforts of DGA-based botnets. We will share the dataset for future research and will also provide a web service to check domains for potential DGA identity.
During exercise, heart rate has proven to be a good measure in planning workouts. It is not only simple to measure but also well understood and has been used for many years for workout planning. To use heart rate to control physical exercise, a model which predicts future heart rate dependent on a given strain can be utilized. In this paper, we present a mathematical model based on convolution for predicting the heart rate response to strain with four physiologically explainable parameters. This model is based on the general idea of the Fitness-Fatigue model for performance analysis, but is revised here for heart rate analysis. Comparisons show that the Convolution model can compete with other known heart rate models. Furthermore, this new model can be improved by reducing the number of parameters. The remaining parameter seems to be a promising indicator of the actual subject’s fitness.
Cognitive robotics aims at understanding biological processes, though it has also the potential to improve future robotics systems. Here we show how a biologically inspired model of motor control with neural fields can be augmented with additional components such that it is able to solve a basic robotics task, that of obstacle avoidance. While obstacle avoidance is a well researched area, the focus here is on the extensibility of a biologically inspired framework. This work demonstrates how easily the biological inspired system can be used to adapt to new tasks. This flexibility is thought to be a major hallmark of biological agents.
WiFi-based Long Distance (WiLD) networks have emerged as a promising alternative approach for Internet in rural areas. However, the MAC layer, which is based on the IEEE802.11 standard, comprises contiguous stations in a cell and is spatially restricted to a few hundred meters at most. In this work, we summarize efforts by different researchers to use IEEE802.11 over long-distances. In addition, we introduce WiLDToken, our solution to optimizing the throughput and fairness and reducing the delay on WiLD links. Compared to previous alternative MAC layers protocols for WiLD, our focus is on optimizing a single link in a multi-radio multi-channel mesh. We implement our protocol in the ns-3 network simulator and show thatWiLDToken is superior to an adapted version of the Distributed Coordination Function (DCF) for different link distances. We find that the throughput on a single link is close to the physical data-rate without a major decrease over longer distances.
Online media consumption is the main driving force for the recent growth of the Web. As especially realtime media is becoming more and more accessible from a wide range of devices, with contrasting screen resolutions, processing resources and network connectivity, a necessary requirement is providing users with a seamless multimedia experience at the best possible quality, henceforth being able to adapt to the specific device and network conditions. This paper introduces a novel approach for adaptive media streaming in the Web. Despite the pervasive pullbased designs based on HTTP, this paper builds upon a Web-native push-based approach by which both the communication and processing overheads are reduced significantly in comparison to the pull-based counterparts. In order to maintain these properties when enhancing the scheme by adaptation features, a server-side monitoring and control needs to be developed as a consequence. Such an adaptive push-based media streaming approach is intr oduced as main contribution of this work. Moreover, the obtained evaluation results provide the evidence that with an adaptive push-based media delivery, on the one hand, an equivalent quality of experience can be provided at lower costs than by adopting pull-based media streaming. On the other hand, an improved responsiveness in switching between quality levels can be obtained at no extra costs.
This paper presents the b-it-bots RoboCup@Work team and its current hardware and functional architecture for the KUKA youBot robot.We describe the underlying software framework and the developed capabilities required for operating in industrial environments including features such as reliable and precise navigation, flexible manipulation and robust object recognition.