Electronic Communications of the EASST (European Association of Software Science and Technology)
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On the Resilience of Opportunistic Networks against DoS Attacks
Full text linkOpportunistic Networks (OppNets) enable contact-based networking and service provisioning when no infrastructure exists, e.g., in disaster areas. In such sensitive scenarios, maintaining their availability is important, but most existing work on OppNets mainly assume fully cooperative and thus not malicious nodes. In this paper, we study the impact of different flavors of low-intensity Denial of Service (DoS) attacks on OppNets, which are hard to detect and to counter. Our results indicate that low-rate DoS and black hole attacks as a special case of DoS, seem to have a huge impact on the packet delivery ratio and the delivery delay of an OppNet
Second Interactive Workshop on the Industrial Application of Verification and Testing, ETAPS 2020 Workshop (InterAVT 2020)
Full text linkTowards QoE-Driven Optimization of Multi-Dimensional Content Streaming
Full text linkWhereas adaptive video streaming for 2D video is well established and frequently used in streaming services, adaptation for emerging higher-dimensional content, such as point clouds, is still a research issue. Moreover, how to optimize resource usage in streaming services that support multiple content types of different dimensions and levels of interactivity has so far not been sufficiently studied. Learning-based approaches aim to optimize the streaming experience according to user needs. They predict quality metrics and try to find system parameters maximizing them given the current network conditions. With this paper, we show how to approach content and network adaption driven by Quality of Experience (QoE) for multi-dimensional content. We describe components required to create a system adapting multiple streams of different content types simultaneously, identify research gaps and propose potential next steps
Towards Optimization-Based Predictive Congestion Control for the Tor Network
Full text linkProviding online anonymity to a broad range of Internet users, the Tor network today faces not only security concerns, but increasingly also performance issues. Due to its multi-hop nature, proper congestion control has been identified to be challenging in this situation. In this paper, we focus on PredicTor, a novel approach towards multi-hop congestion control based on distributed model predictive control (MPC), an advanced optimization-based control technique. We investigate PredicTor's significance for congestion control research. In particular, we carry out a simulation study to evaluate PredicTor's performance in non-trivial network scenarios. Our results indicate the great potential to push the status quo of congestion control, heavily improving achieved latency and fairness. By pointing out benefits and challenges of distributed MPC in this context, we open up a new promising research direction for congestion contro
Discrete event simulation for the purpose of real-time performance evaluation of distributed hardware-in-the-loop simulators for autonomous driving vehicle validation
Full text linkHardware-in-the-loop test benches are distributed computer systems including software, hardware and networking devices, which require strict real-time guarantees. To guarantee strict real-time of the simulator the performance needs to be evaluated. To evaluate the timing performance a discrete event simulation model is built up. The input modeling is based on measurements from the real system in a prototype phase. The results of the simulation model are validated with measurements from a prototype of the real system. The workload is increased until the streaming source becomes unstable, by either exceeding a certain limit of bytes or exceeding the number of parallel software processes running on the cores of the central processing unit. To evaluate the performance beyond these limits, the discrete event simulation model needs to be enriched by a scheduler and a hardware model. To provide real-time guarantees an analytical model needs to be built up
Navigating Communication Networks with Deep Reinforcement Learning
Full text linkTraditional routing protocols such as Open Shortest Path First cannot incorporate fast-changing network states due to their inherent slowness and limited expressiveness. To overcome these limitations, we propose COMNAV, a system that uses Reinforcement Learning (RL) to learn a distributed routing protocol tailored to a specific network. COMNAV interprets routing as a navigational problem, in which flows have to find a way from source to destination. Thus, COMNAV has a close connection to congestion games. The key concept and main contribution is the design of the learning process as a congestion game that allows RL to effectively learn a distributed protocol. Game Theory thereby provides a solid foundation against which the policies RL learns can be evaluated, interpreted, and questioned. We evaluate the capabilities of the learning system in two scenarios in which the routing protocol must react to changes in the network state, and make decisions based on the properties of the flow. Our results show that RL can learn the desired behavior and requires the exchange of only 16 bits of information
Data Serialization Formats for the Internet of Things
Full text linkIoT devices rely on data exchange with gateways and cloud servers. However, the performance of today's serialization formats and libraries on embedded systems with energy and memory constraints is not well-documented and hard to predict. We evaluate (de)serialization and transmission cost of mqtt.eclipse.org payloads on 8- to 32-bit microcontrollers and find that Protocol Buffers (as implemented by NanoPB) and the XDR format, dating back to 1987, are most efficient
Early Warning Identity Threat and Mitigation System
No full textWhile many organizations share threat intelligence, there is still a lack of actionable data for organizations to proactively and effectively respond to emerging identity threats to mitigate a wide range of crimes. There currently exists no solution for organizations to access current trends and intelligence to understand emerging threats and how to appropriately respond to them. This research project delivers I-WARN to help bridge that gap. Using a wide range of open-source information, I-WARN gathers, analyzes, and reports on threats related to the theft, fraud, and abuse of Personally Identifiable Information (PII). I-WARN then maps those threats to the MITRE ATT&CK -- a framework that helps understand lateral movement of an attack -- to offer mitigation and risk reduction tactics. I-WARN aims to deliver actionable intelligence, offering early warning into threat behaviors, and mitigation responses. This paper discusses the technical details of I-WARN, non-exhaustive current solutions for threat intelligence sharing, and future work
Towards opportunistic UAV relaying for smart cities
Full text linkWith recent advances in cooperation among mobile computer systems, Unmanned Aerial Vehicles (UAVs) can be expected to be operated in large numbers and become a part of daily life for a variety of use cases. UAVs are already envi- sioned to act as mobile base stations, e.g., for situations with high node densities and strong communication requirements. However, UAVs are typically imagined to be provided specifically for this purpose. In this work, we study the effects of exploiting randomly passing UAVs at an urban intersection for the communication between vehicles on the ground. We show that a UAV, while following its pri- mary mission, can support cooperative driving vehicles in a purely opportunistic fashion by collecting periodic wireless broadcasts from vehicles and propagating all collected information periodically in an aggregated format. Using simulations, we show that such an opportunistic UAV relaying approach can lead to an improvement of approx. 6 percentage points (% points) in terms of perception of other vehicles
Demonstration: A cloud-native digital twin with adaptive cloud-based control and intrusion detection
Full text linkDigital twins are taking a central role in the industry 4.0 narrative. How-ever, they are still illusive. Many aspects of the digital-twins have yet to materialize.For example, to what degree will they be integrated into cloud and industry 4.0 sys-tems as well as how and if they should augment their physical counterpart. Thosechoices are accompanied by challenging security aspects, many of which have to bestudied partially. In this paper, we present a novel digital-twin demonstrator that en-ables experimentation and advanced research on such systems. The demonstrator iscloud-native, has a distributed adaptive control system, incorporates edge and publicclouds, a PLC, intrusion detection, a wireless network emulator, and an attacker