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On-orbit pointing performance of the Modular Agile Scalable Optical Terminal (MAScOT) for the ILLUMA-T mission

Summary

The Integrated LCRD LEO User Modem and Amplifier Terminal (ILLUMA-T) payload was the first space-based user terminal to demonstrate successful two-way optical communications with a ground terminal via NASA's Laser Communications Relay Demonstration (LCRD). In order to acquire the link, the ILLUMA-T optical module open loop points a wide beacon at the LCRD acquisition sensor. The initial pointing of the beacon is based on real-time ISS position and attitude information and precalculated LCRD ephemeris. This paper examines the on-orbit pointing performance of ILLUMA-T during the mission.
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Summary

The Integrated LCRD LEO User Modem and Amplifier Terminal (ILLUMA-T) payload was the first space-based user terminal to demonstrate successful two-way optical communications with a ground terminal via NASA's Laser Communications Relay Demonstration (LCRD). In order to acquire the link, the ILLUMA-T optical module open loop points a wide beacon...

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Software vulnerability detection using LLM: does additional information help?

Summary

Unlike conventional machine learning (ML) or deep learning (DL) methods, Large Language Models (LLM) possess the ability to tackle complex tasks through intricate chains of reasoning, a facet often overlooked in existing work on vulnerability detection. Nevertheless, these models have demonstrated variable performance when presented with different prompts (inputs), motivating a surge of research into prompt engineering – the process of optimizing prompts to enhance their performance. This paper studies different prompt settings (zero-shot and few-shot) when using LLMs for software vulnerability detection. Our exploration involves harnessing the power of both natural language (NL) unimodal and NL-PL (programming language) bimodal models within the prompt engineering process. Experimental results indicate that LLM, when provided only with source code or zero-shot prompts, tends to classify most code snippets as vulnerable, resulting in unacceptably high recall. These findings suggest that, despite their advanced capabilities, LLMs may not inherently possess the knowledge for vulnerability detection tasks. However, fewshot learning benefits from additional domain-specific knowledge, offering a promising direction for future research in optimizing LLMs for vulnerability detection.
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Summary

Unlike conventional machine learning (ML) or deep learning (DL) methods, Large Language Models (LLM) possess the ability to tackle complex tasks through intricate chains of reasoning, a facet often overlooked in existing work on vulnerability detection. Nevertheless, these models have demonstrated variable performance when presented with different prompts (inputs), motivating...

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Exploiting temporal vulnerabilities for unauthorized access in intent-based networking

Published in:
ACM Conf. on Computer and Communications Security, CCS '24, 14-18 October 2024.

Summary

Intent-based networking (IBN) enables network administrators to express high-level goals and network policies without needing to specify low-level forwarding configurations, topologies, or protocols. Administrators can define intents that capture the overall behavior they want from the network, and an IBN controller compiles such intents into low-level configurations that get installed in the network and implement the desired behavior. We discovered that current IBN specifications and implementations do not specify that flow rule installation orderings should be enforced, which leads to temporal vulnerabilities where, for a limited time, attackers can exploit indeterminate connectivity behavior to gain unauthorized network access. In this paper, we analyze the causes of such temporal vulnerabilities and their security impacts with a representative case study via the ONOS IBN implementation.We devise the Phantom Link attack and demonstrate a working exploit to highlight the security impacts. To defend against such attacks, we propose Spotlight, a detection method that can alert a system administrator of risky intent updates prone to exploitable temporal vulnerabilities. Spotlight is effective in identifying risky updates using realistic network topologies and policies. We show that Spotlight can detect risky updates in a mean time of 0.65 seconds for topologies of over 1,300 nodes.
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Summary

Intent-based networking (IBN) enables network administrators to express high-level goals and network policies without needing to specify low-level forwarding configurations, topologies, or protocols. Administrators can define intents that capture the overall behavior they want from the network, and an IBN controller compiles such intents into low-level configurations that get installed...

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Exploiting temporal vulnerabilities for unauthorized access in intent-based networking

Summary

Intent-based networking (IBN) enables network administrators to express high-level goals and network policies without needing to specify low-level forwarding configurations, topologies, or protocols. Administrators can define intents that capture the overall behavior they want from the network, and an IBN controller compiles such intents into low-level configurations that get installed in the network and implement the desired behavior. We discovered that current IBN specifications and implementations do not specify that flow rule installation orderings should be enforced, which leads to temporal vulnerabilities where, for a limited time, attackers can exploit indeterminate connectivity behavior to gain unauthorized network access. In this paper, we analyze the causes of such temporal vulnerabilities and their security impacts with a representative case study via the ONOS IBN implementation. We devise the Phantom Link attack and demonstrate a working exploit to highlight the security impacts. To defend against such attacks, we propose Spotlight, a detection method that can alert a system administrator of risky intent updates prone to exploitable temporal vulnerabilities. Spotlight is effective in identifying risky updates using realistic network topologies and policies. We show that Spotlight can detect risky updates in a mean time of 0.65 seconds for topologies of over 1,300 nodes.
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Summary

Intent-based networking (IBN) enables network administrators to express high-level goals and network policies without needing to specify low-level forwarding configurations, topologies, or protocols. Administrators can define intents that capture the overall behavior they want from the network, and an IBN controller compiles such intents into low-level configurations that get installed...

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Manipulative interference attacks

Summary

A μ-kernel is an operating system (OS) paradigm that facilitates a strong cybersecurity posture for embedded systems. Unlike a monolithic OS such as Linux, a μ-kernel reduces overall system privilege by deploying most OS functionality within isolated, userspace protection domains. Moreover, a μ-kernel ensures confidentiality and integrity between protection domains (i.e., spatial isolation), and offers timing predictability for real-time tasks in mixed-criticality systems (i.e., temporal isolation). One popular μ-kernel is seL4 which offers extensive formal guarantees of implementation correctness and flexible temporal budgeting mechanisms. However, we show that an untrusted protection domain on a μ-kernel can abuse service requests to other protection domains in order to corrode system availability. We generalize this denial-of-service (DoS) attack strategy as Manipulative Interference Attacks (MIAs) and introduce techniques to efficiently identify instances of MIAs within a configured system. Specifically, we propose a novel hybrid approach that first leverages static analysis to identify software components with influenceable execution times, and second, uses an automatically generated model-based analysis to determine which compromised protection domains can manipulate the influenceable components and trigger MIAs. We investigate the risk of MIAs in several representative system examples including the seL4 Microkit, as well as a case study of seL4 software artifacts from the DARPA Cyber Assured Systems Engineering (CASE) program. In particular, we demonstrate that our analysis is efficient enough to discover practical instances of MIAs in real-world systems.
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Summary

A μ-kernel is an operating system (OS) paradigm that facilitates a strong cybersecurity posture for embedded systems. Unlike a monolithic OS such as Linux, a μ-kernel reduces overall system privilege by deploying most OS functionality within isolated, userspace protection domains. Moreover, a μ-kernel ensures confidentiality and integrity between protection domains...

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ECP 0857P final report for the NEXRAD ROC: Modified VCP 35

Published in:
MIT Lincoln Laboratory Report ATC-456

Summary

This report responds to a request by the NEXRAD ROC through the FAA to close out ECP0857P in their records. It details the motivation for the modification to the radar coverage pattern called VCP 35, its deployment, and use coordinated with nearby in situ ICICLE flight missions or independent of those. Recommendations are included for future considerations to modify VCP 35.
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Summary

This report responds to a request by the NEXRAD ROC through the FAA to close out ECP0857P in their records. It details the motivation for the modification to the radar coverage pattern called VCP 35, its deployment, and use coordinated with nearby in situ ICICLE flight missions or independent of...

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Variability of speech timing features across repeated recordings: a comparison of open-source extraction techniques

Summary

Variations in speech timing features have been reliably linked to symptoms of various health conditions, demonstrating clinical potential. However, replication challenges hinder their
translation; extracted speech features are susceptible to methodological variations in the recording and processing pipeline. Investigating this, we compared exemplar timing features extracted via three different techniques from recordings of healthy speech. Our results show that features extracted via an intensity-based method differ from those produced by forced alignment. Different extraction methods also led to differing estimates of within-speaker feature variability over time in an analysis of recordings repeated systematically over three sessions in one day (n=26) and in one week (n=28). Our findings highlight the importance of feature extraction in study design and interpretation, and the need for consistent, accurate extraction techniques for clinical research.
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Summary

Variations in speech timing features have been reliably linked to symptoms of various health conditions, demonstrating clinical potential. However, replication challenges hinder their
translation; extracted speech features are susceptible to methodological variations in the recording and processing pipeline. Investigating this, we compared exemplar timing features extracted via three different techniques...

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VulSim: Leveraging similarity of multi-dimensional neighbor embeddings for vulnerability detection

Summary

Despite decades of research in vulnerability detection, vulnerabilities in source code remain a growing problem, and more effective techniques are needed in this domain. To enhance software vulnerability detection, in this paper, we first show that various vulnerability classes in the C programming language share common characteristics, encompassing semantic, contextual, and syntactic properties. We then leverage this knowledge to enhance the learning process of Deep Learning (DL) models for vulnerability detection when only sparse data is available. To achieve this, we extract multiple dimensions of information from the available, albeit limited, data. We then consolidate this information into a unified space, allowing for the identification of similarities among vulnerabilities through nearest-neighbor embeddings. The combination of these steps allows us to improve the effectiveness and efficiency of vulnerability detection using DL models. Evaluation results demonstrate that our approach surpasses existing State-of-the-art (SOTA) models and exhibits strong performance on unseen data, thereby enhancing generalizability.
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Summary

Despite decades of research in vulnerability detection, vulnerabilities in source code remain a growing problem, and more effective techniques are needed in this domain. To enhance software vulnerability detection, in this paper, we first show that various vulnerability classes in the C programming language share common characteristics, encompassing semantic, contextual...

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Building digital twins for cardiovascular health: From principles to clinical impact

Summary

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2) map all available data streams to the trajectories of disease states over the patient's lifetime; and (3) apply this information for optimal clinical interventions and outcomes. Here we review new advances that may address these challenges using digital twin technology to fulfill the promise of personalized cardiovascular medical practice. Rooted in engineering mechanics and manufacturing, the digital twin is a virtual representation engineered to model and simulate its physical counterpart. Recent breakthroughs in scientific computation, artificial intelligence, and sensor technology have enabled rapid bidirectional interactions between the virtual-physical counterparts with measurements of the physical twin that inform and improve its virtual twin, which in turn provide updated virtual projections of disease trajectories and anticipated clinical outcomes. Verification, validation, and uncertainty quantification builds confidence and trust by clinicians and patients in the digital twin and establishes boundaries for the use of simulations in cardiovascular medicine. Mechanistic physiological models form the fundamental building blocks of the personalized digital twin that continuously forecast optimal management of cardiovascular health using individualized data streams. We present exemplars from the existing body of literature pertaining to mechanistic model development for cardiovascular dynamics and summarize existing technical challenges and opportunities pertaining to the foundation of a digital twin.
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Summary

The past several decades have seen rapid advances in diagnosis and treatment of cardiovascular diseases and stroke, enabled by technological breakthroughs in imaging, genomics, and physiological monitoring, coupled with therapeutic interventions. We now face the challenge of how to (1) rapidly process large, complex multimodal and multiscale medical measurements; (2)...

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Impacts of WSR-88D SAILS and MRLE VCP options on severe weather warning performance

Published in:
MIT Lincoln Laboratory Report NOAA-36
Topic:

Summary

The impacts of supplemental adaptive intra-volume low-level scan (SAILS) and mid-volume rescan of low-level elevations (MRLE) usage on the Weather Surveillance Radar 1988-Doppler (WSR-88D) with respect to severe weather warning performance were evaluated. This is an update and expansion of an earlier study by Cho et al. (2022). Statistical methods applied to historical data from 2014–2022 yielded the following major results. Severe thunderstorm (SVR) warning performance metrics are shown in the figure below, where the vertical bars represent 95% confidence intervals and the numbers at the bottom correspond to the sample sizes. The results are divided according to the scanning option that is estimated to have been used at the time the decision to issue (or not issue) a warning was made. The first point to note is that probability of detection (POD), false alarm ratio (FAR), and mean lead time (MLT) improvements were associated with the usage of supplemental adaptive intra-volume low-level scan (SAILS or MRLE) in a statistically meaningful manner. As for the different sub-modes of SAILS, the multiple elevation scan option (MESO), i.e., SAILSx2 and SAILSx3, appeared to give more benefit than SAILSx1. However, the fact that the fastest base-scan update rates provided by SAILSx3 hardly yielded more benefit than SAILSx2 may indicate that the slowdown in volume scan update rates counteracted the more frequent base scans when going from SAILSx2 to SAILSx3. For POD and FAR, MRLE+4 significantly outperformed MESO-SAILS, which may also indicate that more frequent updates of elevations angle scans higher than the lowest tilt are needed by forecasters to make accurate SVR warning decisions.
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Summary

The impacts of supplemental adaptive intra-volume low-level scan (SAILS) and mid-volume rescan of low-level elevations (MRLE) usage on the Weather Surveillance Radar 1988-Doppler (WSR-88D) with respect to severe weather warning performance were evaluated. This is an update and expansion of an earlier study by Cho et al. (2022). Statistical methods...

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