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This paper introduces a scenario where a maneuverable satellite in geostationary orbit (GEO) conducts on-orbit attacks, targeting communication between a GEO satellite and a ground station, with the ability to switch between stationary and time-variant jamming modes. We propose a machine learning-based detection approach, employing the random forest algorithm with principal component analysis (PCA) to enhance detection accuracy in the stationary model. At the same time, an adaptive threshold-based technique is implemented for the time-variant model to detect dynamic jamming events effectively. Our methodology emphasizes the need for the use of orbital dynamics in integrating physical constraints from satellite dynamics to improve model robustness and detection accuracy. Simulation results highlight the effectiveness of PCA in enhancing the performance of the stationary model, while the adaptive thresholding method achieves high accuracy in detecting jamming in the time-variant scenario. This approach provides a robust solution for mitigating the evolving threats to satellite communication in GEO environments.

The rapid evolution of communication technologies, compounded by recent geopolitical events such as the Viasat cyberattack in February 2022, has highlighted the urgent need for fast and reliable satellite missions for military and civil security operations. Consequently, this paper examines two Earth observation (EO) missions: one utilizing a single low Earth orbit (LEO) satellite and another through a network of LEO satellites, employing a secure-by-component design strategy. This approach begins by defining the scope of technical security engineering, decomposing the system into components and data flows, and enumerating attack surfaces. Then it proceeds by identifying threats to low-level components, applying secure-by-design principles, redesigning components into secure blocks in alignment with the Space Attack Research & Tactic Analysis (SPARTA) framework, and crafting shall statements to refactor the system design, with a particular focus on improving the security of the link segment.

As space becomes increasingly crowded and contested, robust autonomous capabilities for multi-agent environments are gaining critical importance. Current autonomous systems in space primarily rely on optimization-based path planning or long-range orbital maneuvers, which have not yet proven effective in adversarial scenarios where one satellite is actively pursuing another. We introduce Divergent Adversarial Reinforcement Learning (DARL), a two-stage Multi-Agent Reinforcement Learning (MARL) approach designed to train autonomous evasion strategies for satellites engaged with multiple adversarial spacecraft. Our method enhances exploration during training by promoting diverse adversarial strategies, leading to more robust and adaptable evader models. We validate DARL through a cat-and-mouse satellite scenario, modeled as a partially observable multi-agent capture the flag game where two adversarial `cat' spacecraft pursue a single `mouse' evader. DARL's performance is compared against several benchmarks, including an optimization-based satellite path planner, demonstrating its ability to produce highly robust models for adversarial multi-agent space environments.

Space Domain Awareness (SDA) has become increasingly vital with the rapid growth of commercial space activities and the expansion of New Space. This paper stresses the necessity of transitioning from centralized to distributed SDA architectures. The current architecture predominantly relies on individual downhaul, which we propose to transition to on-orbit distribution. Our results demonstrate that the individual downhaul architecture does not scale efficiently with the increasing number of nodes, while on-orbit distribution offers significant improvements. By comparing the centralized architecture with the proposed distributed architecture, we highlight the advantages of enhanced coverage and resilience. Our findings show that on-orbit distribution greatly outperforms individual downhaul in terms of latency and scalability. Specifically, the latency results for on-orbit distribution are substantially lower and more consistent, even as the number of satellites increases. In addition, we address the inherent challenges associated with on-orbit distribution architecture, particularly cybersecurity concerns. We focus on link security to ensure the availability and integrity of data transmission in these advanced SDA systems. Future expectations include further refinement of on-orbit distribution strategies and the development of robust cybersecurity measures to support the scalability and resilience of SDA systems., Comment: 12 pages, 3 figures

Spacecraft are among the earliest autonomous systems. Their ability to function without a human in the loop have afforded some of humanity's grandest achievements. As reliance on autonomy grows, space vehicles will become increasingly vulnerable to attacks designed to disrupt autonomous processes-especially probabilistic ones based on machine learning. This paper aims to elucidate and demonstrate the threats that adversarial machine learning (AML) capabilities pose to spacecraft. First, an AML threat taxonomy for spacecraft is introduced. Next, we demonstrate the execution of AML attacks against spacecraft through experimental simulations using NASA's Core Flight System (cFS) and NASA's On-board Artificial Intelligence Research (OnAIR) Platform. Our findings highlight the imperative for incorporating AML-focused security measures in spacecraft that engage autonomy., Comment: Preprint

In March of 2022, Network battalion 65 (NB65), a hacktivist affiliate of Anonymous, publicly asserted its successful breach of ROSCOSMOS's satellite imaging capabilities in response to Russia's invasion of Ukraine. NB65 disseminated a series of primary sources as substantiation, proclaiming the incapacitation of ROSCOSMOS's space-based vehicle monitoring system and doxing of related proprietary documentation. Despite the profound implications of hacktivist incursions into the space sector, the event has garnered limited attention due to the obscurity of technical attack vectors and ROCOSMOS's denial of NB65's allegations. Through analysis of NB65's released primary sources of evidence, this paper uncovers the probable vulnerabilities and exploits that enabled the alleged breach into ROSCOSMOS's ground and space segment. Additionally, we highlight lessons learned and the consequences this event has for the global aerospace community.

Cyber-Physical systems (CPS) have complex lifecycles involving multiple stakeholders, and the transparency of both hardware and software components' supply chain is opaque at best. This raises concerns for stakeholders who may not trust that what they receive is what was requested. There is an opportunity to build a cyberphysical titling process offering universal traceability and the ability to differentiate systems based on provenance. Today, RFID tags and barcodes address some of these needs, though they are easily manipulated due to non-linkage with an object or system's intrinsic characteristics. We propose cyberphysical sequencing as a low-cost, light-weight and pervasive means of adding track-and-trace capabilities to any asset that ties a system's physical identity to a unique and invariant digital identifier. CPS sequencing offers benefits similar Digital Twins' for identifying and managing the provenance and identity of an asset throughout its life with far fewer computational and other resources., Comment: 14 pages, 6 figures

Spending on cybersecurity products and services is expected to top 123 billion U.S. dollars for 2020, more than double the 55 billion U.S. dollars spent in 2011.1 In that same period, cyber breaches quadrupled. Organizations globally face increasing liabilities, while boards of directors grapple with a seemingly Sisyphean challenge. Cyber Crossroads was born out of these alarming trends and a realization that the world cannot go on funneling finite resources into an indefinite, intractable problem. Cyber Crossroads brings together expertise from across the world, spanning aspects of the cyber problem (including technology, legal, risk, and economic) with the goal of creating a Cyber Standard of Care built through a global, not-for-profit research collaborative with no commercial interests. A Cyber Standard of Care should be applicable across industries and regardless of the organization size. It should be practical and implementable, with no requirement to purchase any product/service. Cyber Standard of Care should be woven into the existing governance fabric of the organization and it should not be yet another technical checklist, but a process/governance framework that can stand over time. To achieve this, we engaged with cyber risk experts and practitioners with a variety of relevant expertise, secured the advice/guidance of regulators and legal experts across jurisdictions, and interviewed leaders from 56 organizations globally to understand their challenges and identify best practices.

A smart city involves critical infrastructure systems that have been digitally enabled. Increasingly, many smart city cyber-physical systems are becoming automated. The extent of automation ranges from basic logic gates to sophisticated, artificial intelligence (AI) that enables fully autonomous systems. Because of modern society's reliance on autonomous systems in smart cities, it is crucial for them to operate in a safe manner; otherwise, it is feasible for these systems to cause considerable physical harm or even death. Because smart cities could involve thousands of autonomous systems operating in concert in densely populated areas, safety assurances are required. Challenges abound to consistently manage the safety of such autonomous systems due to their disparate developers, manufacturers, operators and users. A novel network and a sample of associated network functions for autonomous systems is proposed that aims to provide a baseline of safety for autonomous systems. This is accomplished by establishing a custom-designed network for autonomous systems that is separate from the Internet, and can handle certain functions that enable safety through active networking. Such a network design sits at the margins of the end-to-end principle, which is warranted considering the safety of autonomous systems is at stake as is argued in this paper. Without a scalable safety strategy for autonomous systems as proposed, assured autonomy in smart cities will remain elusive., Comment: PREPRINT, 6 Pages

Automotive software is increasingly complex and critical to safe vehicle operation, and related embedded systems must remain up-to-date to ensure long-term system performance. Update mechanisms and data modification tools introduce opportunities for malicious actors to compromise these cyber-physical systems, and for trusted actors to mistakenly install incompatible software versions. A distributed and stratified "black box" audit trail for automotive software and data provenance is proposed to assure users, service providers, and original equipment manufacturers (OEMs) of vehicular software integrity and reliability. The proposed black box architecture is both layered and diffuse, employing distributed hash tables (DHT), a parity system and a public blockchain to provide high resilience, assurance, scalability, and efficiency for automotive and other high-assurance systems., Comment: 12 pages, 2 figures