Exponentially-declining launch costs have led to an explosion of inexpensive satellites launched to space, often equipped with off-the-shelf chips. These chips, however, lack hardware radiation protection, leaving them vulnerable to space radiation. We thus design Radshield, a software system protecting against the two most ubiquitous and costly radiation fault scenarios: (a) radiation-induced short-circuits that lead to permanent hardware failure; and (b) radiation-induced transient charges that result in single-bit silent data corruption (SDC). Radshield counters these failure scenarios with two components. First, it uses a short-circuit detector that can detect tiny increases in the device's current draw by estimating the normal current draw when resource utilization is low. Second, it duplicates the execution of spacecraft workloads in a CPU and memory-efficient manner, and catches SDCs even when they affect the CPU's pipeline or cache. In our experiments, we show Radshield is very effective at preventing both errors, and is 1.4-35.5× more power-efficient than the state-of-the-art protection mechanisms in detecting SDC. Radshield is deployed on missions in low-earth orbit and in deep space.

Satellite-backed services have become an essential component of everyday life, in areas such as navigation, Internet connectivity and imaging. The collapsing cost of launching to space has disrupted the way satellites are deployed, shifting the industry from a model of few expensive fault-tolerant high-orbit satellites to arrays of commodity low-cost SmallSats in low-Earth orbit. However, satellite software hasn't kept up with the hardware trends, and missions are still using the ad-hoc flight software infrastructure built for expensive one-off missions in high-altitude orbits, wherein operators manually deploy software to each satellite individually. This approach is woefully inadequate in the new emerging SmallSat operational model, where an operator needs to manage hundreds of "wimpy" satellites with varying hardware capabilities under intermittent communication. Furthermore, SmallSat operators increasingly "rent out" their infrastructure to third parties, and need to support the workloads of multiple different tenants on the same satellites, which raises the classic problems of isolation and security similar to cloud computing, but in the much more constrained hardware environment of space. In this paper, we describe the new research questions introduced by this operational model. We also sketch the design of a novel lightweight eBPF-based runtime for fleets of multi-tenant, heterogeneous and intermittently-connected satellites.

ABSTRACT As pneumonia cases continue to rise worldwide, rapid diagnostic capabilities are essential for effective treatment. However, traditional medical systems often lack efficiency and coordinated management. In response, we propose an AI‐driven biomedical diagnosis platform for real‐time detection and swift intervention. Leveraging privacy‐preserving deep learning on the edge, users can promptly obtain automated diagnoses by uploading chest CT images. To further enhance accuracy, we employ a federated learning (FL) framework that ensures scalable training in an industrial IoT setting while protecting patient data. Our global FL model achieves around 96.25% accuracy on a validation dataset, outperforming individual clients by 3.42%. By eliminating the need for sharing raw data, patient privacy is preserved, and the system offers improved flexibility and scalability for medical diagnosis.

The development of the Internet of Things (IoT) has led to the widespread use of WiFi-enabled consumer electronic devices, which are now common in everyday life. These advancements in IoT have greatly improved data collection and analysis capabilities, especially for health monitoring applications. However, traditional centralized machine learning methods often fall short, raising significant privacy concerns and requiring extensive data collection, which is inefficient. To address these limitations within the distributed IoT environment, this article presents a federated learning (FL)-based WiFi sensing system specifically designed for health monitoring. By enabling local model training, our system prevents the sharing of sensitive data, thus reducing the risk of privacy breaches. We further enhance our system with a secret sharing mechanism coupled with model sparsification to significantly improve privacy. Additionally, our improved top-k model sparsification algorithm, equipped with adaptive residuals, reduces communication overhead while ensuring high accuracy. Extensive testing across various datasets and models confirms that our system outperforms existing benchmarks in terms of privacy protection and communication efficiency, marking a substantial advancement in health monitoring within the IoT.

To achieve longer driving distances, planetary robotics missions require accurate localization to counteract position uncertainty. Freedom and precision in driving allows scientists to reach and study sites of interest. Typically, rover global localization has been performed manually by humans, which is accurate but time-consuming as data is relayed between planets. This paper describes a global localization algorithm that is run onboard the Perseverance Mars rover. Our approach matches rover images to orbital maps using a modified census transform to achieve sub-meter accurate, near-human localization performance on a real dataset of 264 Mars rover panoramas. The proposed solution has also been successfully executed on the Perseverance Mars Rover, demonstrating the practicality of our approach.

Due to their low cost and the need to run computationally-intensive algorithms locally, satellites and spacecraft are increasingly employing off-the-shelf computing hardware. However, hardware in space is exposed to significantly higher amounts of radiation than on Earth, potentially destroying the hardware or causing it to output incorrect results. We envision that solely using software fault tolerance techniques, commodity hardware operating in space can achieve fault tolerance equivalent or close to expensive and slow radiation-hardened hardware. To achieve this goal, we need to address the two main radiation fault scenarios: hardware overheating and silent data corruption. We provide preliminary data on the effects of these errors, and introduce a set of techniques to address them. Enabling the full use of commodity hardware in space holds the promise of improving the compute capabilities and cost effectiveness of low-earth orbit satellites by orders of magnitude.

Exploit-based denial-of-service attacks (exDoS) are challenging to detect and mitigate. Rather than flooding the network with excessive traffic, these attacks generate low rates of application requests that exploit some vulnerability and tie up a scarce key resource. It is impractical to design defenses for each variant of exDoS attacks separately. This approach does not scale, since new vulnerabilities can be discovered in existing applications, and new applications can be deployed with yet unknown vulnerabilities.

Programs and services relying on weak hash algorithms as part of their hash table implementations are vulnerable to hash-collision denial-of-service attacks. In the context of such an attack, the attacker sends a series of program inputs leading to hash collisions. In the best case, this slows down the execution and processing for all requests, and in the worst case it renders the program or service unavailable. We propose a new binary program analysis approach to automatically detect weak hash functions and patch vulnerable binary programs, by replacing the weak hash function with a secure alternative. To verify that our mitigation strategy does not break program functionality, we design and leverage multiple stages of static analysis and symbolic execution, which demonstrate that the patched code performs equivalently to the original code, but does not suffer from the same vulnerability. We analyze 105,831 real-world programs and confirm the use of 796 weak hash functions in the same number of programs. We successfully replace 759 of these in a non-disruptive manner. The entire process is automated. Among the real-world programs analyzed, we discovered, disclosed and mitigated a zero-day hash-collision vulnerability in Reddit.