As space exploration advances and the commercialization and militarization of space technologies expand, ensuring the security of space assets has become a paramount concern. A key factor contributing to this challenge is the growing reliance on off-the-shelf hardware and software. While such components accelerate the adoption and commercial use of space technologies, they also introduce new vulnerabilities and broaden the attack surface. This paper highlights the critical importance of integrating cybersecurity concepts throughout the entire design lifecycle of space systems. It examines key dimensions of secure space system development, including secure engineering practices, comprehensive testing methodologies, strategies for cyber resiliency, and the role of standardization in fostering a consistent and robust security posture across the industry. By addressing these essential aspects, the paper underscores the need for a holistic, lifecycle-driven approach to safeguarding space systems against evolving cyber threats.

Due to the rapid development of autonomous driving, the Internet of Things and streaming services, modern communication systems have to cope with varying channel conditions and a steadily rising number of users and devices. This, and the still rising bandwidth demands, can only be met by intelligent network automation, which requires highly flexible and blind transceiver algorithms. To tackle those challenges, we propose a novel adaptive equalization scheme, which exploits the prosperous advances in deep learning by training an equalizer with an adversarial network. The learning is only based on the statistics of the transmit signal, so it is blind regarding the actual transmit symbols and agnostic to the channel model. The proposed approach is independent of the equalizer topology and enables the application of powerful neural network based equalizers. In this work, we prove this concept in simulations of different―both linear and nonlinear―transmission channels and demonstrate the capability of the proposed blind learning scheme to approach the performance of non-blind equalizers. Furthermore, we provide a theoretical perspective and highlight the challenges of the approach.

We address the joint optimization of multiple stream joins in a scale-out architecture by tailoring prior work on multi-way stream joins to predicate-driven data partitioning schemes. We present an integer linear programming (ILP) formulation for selecting the partitioning and tuple routing with minimal probe load. The presented algorithms and optimization schemes are implemented in CLASH, a data stream processor developed in our group that translates queries to deployable Apache Storm topologies after optimization. The experiments conducted on TPC-H data exhibit the potential of multi-query optimization of multi-way stream joins and the effectiveness and feasibility of the ILP optimization problem.

We propose MultiStream, a novel multi-way join operator that optimizes tuple-routing schemes across materialized relations and intermediate results to compute the join results. It allows trading off materialization effort vs. network utilization, which is essential to tailoring the query evaluation to the quantity and power of available compute resources. This goes far beyond the capabilities of more rigid schemes like the HyperCube technique or the BiStream approach that is limited to simple binary plans only. Around MultiStream, we have implemented CLASH, a high-level abstraction on top of Apache Storm, allowing users to phrase declarative queries, which are then optimized and translated into executable Storm topologies. Experiments on Amazon's EC2, using TPC-H and an additional synthetic benchmark, underpin the versatility of the operator model and CLASH'S ability to outperform state-of-the-art competitors.

We propose the demonstration of CLASH, a high-level abstraction on top of Apache Storm. CLASH is designed around MultiStream, a novel join operator designed for native support of distributed, multi-way stream joins. MultiStream allows trading off materialization of intermediate results versus communication load. With this demonstration, we invite the audience to explore the full potential of CLASH: multi-way stream joins, creation of complex join plans and their automated optimization, and ultimately the hassle-free SQL-style user/application interface and the translation of the optimized query plans to deployable Storm topologies that are executed on our local compute cluster.