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Keeping Safe Rust safe with Galeed

Published in:
Annual Computer Security Applications Conf., ACSAC, December 2021, pp. 824-36.

Summary

Rust is a programming language that simultaneously offers high performance and strong security guarantees. Safe Rust (i.e., Rust code that does not use the unsafe keyword) is memory and type safe. However, these guarantees are violated when safe Rust interacts with unsafe code, most notably code written in other programming languages, including in legacy C/C++ applications that are incrementally deploying Rust. This is a significant problem as major applications such as Firefox, Chrome, AWS, Windows, and Linux have either deployed Rust or are exploring doing so. It is important to emphasize that unsafe code is not only unsafe itself, but also it breaks the safety guarantees of ‘safe’ Rust; e.g., a dangling pointer in a linked C/C++ library can access and overwrite memory allocated to Rust even when the Rust code is fully safe. This paper presents Galeed, a technique to keep safe Rust safe from interference from unsafe code. Galeed has two components: a runtime defense to prevent unintended interactions between safe Rust and unsafe code and a sanitizer to secure intended interactions. The runtime component works by isolating Rust’s heap from any external access and is enforced using Intel Memory Protection Key (MPK) technology. The sanitizer uses a smart data structure that we call pseudo-pointer along with automated code transformation to avoid passing raw pointers across safe/unsafe boundaries during intended interactions (e.g., when Rust and C++ code exchange data). We implement and evaluate the effectiveness and performance of Galeed via micro- and macro-benchmarking, and use it to secure a widely used component of Firefox.
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Summary

Rust is a programming language that simultaneously offers high performance and strong security guarantees. Safe Rust (i.e., Rust code that does not use the unsafe keyword) is memory and type safe. However, these guarantees are violated when safe Rust interacts with unsafe code, most notably code written in other programming...

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Detecting pathogen exposure during the non-symptomatic incubation period using physiological data: proof of concept in non-human primates

Summary

Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First, we seek to determine the upper limits of early warning detection through physiological measurements. Second, we investigate whether the detected physiological response is specific to the pathogen. Third, we explore the feasibility of extending early warning detection with wearable devices. Research Methods: For the first objective, we developed a supervised random forest algorithm to detect pathogen exposure in the asymptomatic period prior to overt symptoms (fever). We used high-resolution physiological telemetry data (aortic blood pressure, intrathoracic pressure, electrocardiograms, and core temperature) from non-human primate animal models exposed to two viral pathogens: Ebola and Marburg (N = 20). Second, to determine reusability across different pathogens, we evaluated our algorithm against three independent physiological datasets from non-human primate models (N = 13) exposed to three different pathogens: Lassa and Nipah viruses and Y. pestis. For the third objective, we evaluated performance degradation when the algorithm was restricted to features derived from electrocardiogram (ECG) waveforms to emulate data from a non-invasive wearable device. Results: First, our cross-validated random forest classifier provides a mean early warning of 51 ± 12 h, with an area under the receiver-operating characteristic curve (AUC) of 0.93 ± 0.01. Second, our algorithm achieved comparable performance when applied to datasets from different pathogen exposures – a mean early warning of 51 ± 14 h and AUC of 0.95 ± 0.01. Last, with a degraded feature set derived solely from ECG, we observed minimal degradation – a mean early warning of 46 ± 14 h and AUC of 0.91 ± 0.001. Conclusion: Under controlled experimental conditions, physiological measurements can provide over 2 days of early warning with high AUC. Deviations in physiological signals following exposure to a pathogen are due to the underlying host’s immunological response and are not specific to the pathogen. Pre-symptomatic detection is strong even when features are limited to ECG-derivatives, suggesting that this approach may translate to non-invasive wearable devices.
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Summary

Background and Objectives: Early warning of bacterial and viral infection, prior to the development of overt clinical symptoms, allows not only for improved patient care and outcomes but also enables faster implementation of public health measures (patient isolation and contact tracing). Our primary objectives in this effort are 3-fold. First...

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Unsupervised Bayesian adaptation of PLDA for speaker verification

Published in:
Interspeech, 30 August - 3 September 2021.

Summary

This paper presents a Bayesian framework for unsupervised domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA). By interpreting class labels as latent random variables, Variational Bayes (VB) is used to derive a maximum a posterior (MAP) solution of the adapted PLDA model when labels are missing, referred to as VB-MAP. The VB solution iteratively infers class labels and updates PLDA hyperparameters, offering a systematic framework for dealing with unlabeled data. While presented as a general solution, this paper includes experimental results for domain adaptation in speaker verification. VBMAP estimation is applied to the 2016 and 2018 NIST Speaker Recognition Evaluations (SREs), both of which included small and unlabeled in-domain data sets, and is shown to provide performance improvements over a variety of state-of-the-art domain adaptation methods. Additionally, VB-MAP estimation is used to train a fully unsupervised PLDA model, suffering only minor performance degradation relative to conventional supervised training, offering promise for training PLDA models when no relevant labeled data exists.
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Summary

This paper presents a Bayesian framework for unsupervised domain adaptation of Probabilistic Linear Discriminant Analysis (PLDA). By interpreting class labels as latent random variables, Variational Bayes (VB) is used to derive a maximum a posterior (MAP) solution of the adapted PLDA model when labels are missing, referred to as VB-MAP...

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Practical principle of least privilege for secure embedded systems

Published in:
2021 IEEE 27th Real-Time and Embedded Technology and Applications Symp., RTAS. 18-21 May 2021.

Summary

Many embedded systems have evolved from simple bare-metal control systems to highly complex network-connected systems. These systems increasingly demand rich and feature-full operating-systems (OS) functionalities. Furthermore, the network connectedness offers attack vectors that require stronger security designs. To that end, this paper defines a prototypical RTOS API called Patina that provides services common in featurerich OSes (e.g., Linux) but absent in more trustworthy u-kernel-based systems. Examples of such services include communication channels, timers, event management, and synchronization. Two Patina implementations are presented, one on Composite and the other on seL4, each of which is designed based on the Principle of Least Privilege (PoLP) to increase system security. This paper describes how each of these u-kernels affect the PoLP-based design, as well as discusses security and performance tradeoffs in the two implementations. Results of comprehensive evaluations demonstrate that the performance of the PoLP-based implementation of Patina offers comparable or superior performance to Linux, while offering heightened isolation.
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Summary

Many embedded systems have evolved from simple bare-metal control systems to highly complex network-connected systems. These systems increasingly demand rich and feature-full operating-systems (OS) functionalities. Furthermore, the network connectedness offers attack vectors that require stronger security designs. To that end, this paper defines a prototypical RTOS API called Patina that...

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A cybersecurity moonshot

Author:
Published in:
IEEE Secur. Priv., Vol. 19, No. 3, May-June 2021, pp. 8-16.

Summary

Cybersecurity needs radical rethinking to change its current landscape. This article charts a vision for a cybersecurity moonshot based on radical but feasible technologies that can prevent the largest classes of vulnerabilities in modern systems.
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Summary

Cybersecurity needs radical rethinking to change its current landscape. This article charts a vision for a cybersecurity moonshot based on radical but feasible technologies that can prevent the largest classes of vulnerabilities in modern systems.

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PATHATTACK: attacking shortest paths in complex networks

Summary

Shortest paths in complex networks play key roles in many applications. Examples include routing packets in a computer network, routing traffic on a transportation network, and inferring semantic distances between concepts on the World Wide Web. An adversary with the capability to perturb the graph might make the shortest path between two nodes route traffic through advantageous portions of the graph (e.g., a toll road he owns). In this paper, we introduce the Force Path Cut problem, in which there is a specific route the adversary wants to promote by removing a minimum number of edges in the graph. We show that Force Path Cut is NP-complete, but also that it can be recast as an instance of the Weighted Set Cover problem, enabling the use of approximation algorithms. The size of the universe for the set cover problem is potentially factorial in the number of nodes. To overcome this hurdle, we propose the PATHATTACK algorithm, which via constraint generation considers only a small subset of paths|at most 5% of the number of edges in 99% of our experiments. Across a diverse set of synthetic and real networks, the linear programming formulation of Weighted Set Cover yields the optimal solution in over 98% of cases. We also demonstrate a time/cost tradeoff using two approximation algorithms and greedy baseline methods. This work provides a foundation for addressing similar problems and expands the area of adversarial graph mining beyond recent work on node classification and embedding.
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Summary

Shortest paths in complex networks play key roles in many applications. Examples include routing packets in a computer network, routing traffic on a transportation network, and inferring semantic distances between concepts on the World Wide Web. An adversary with the capability to perturb the graph might make the shortest path...

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Health-informed policy gradients for multi-agent reinforcement learning

Summary

This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then extended to a multi-agent variant of the proximal policy optimization algorithm and demonstrated on simple particle environments that have elements of system health, risk-taking, semi-expendable agents, and partial observability. We show significant improvement in learning performance compared to policy gradient methods that do not perform multi-agent credit assignment.
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Summary

This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is then...

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Combating Misinformation: HLT Highlights from MIT Lincoln Laboratory

Published in:
Human Language Technology Conference (HLTCon), 16-18 March 2021.

Summary

Dr. Joseph Campbell shares several human language technologies highlights from MIT Lincoln Laboratory. These include key enabling technologies in combating misinformation to link personas, analyze content, and understand human networks. Developing operationally relevant technologies requires access to corresponding data with meaningful evaluations, as Dr. Douglas Reynolds presented in his keynote. As Dr. Danelle Shah discussed in her keynote, it’s crucial to develop these technologies to operate at deeper levels than the surface. Producing reliable information from the fusion of missing and inherently unreliable information channels is paramount. Furthermore, the dynamic misinformation environment and the coevolution of allied methods with adversarial methods represent additional challenges
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Summary

Dr. Joseph Campbell shares several human language technologies highlights from MIT Lincoln Laboratory. These include key enabling technologies in combating misinformation to link personas, analyze content, and understand human networks. Developing operationally relevant technologies requires access to corresponding data with meaningful evaluations, as Dr. Douglas Reynolds presented in his keynote...

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Combating Misinformation: What HLT Can (and Can't) Do When Words Don't Say What They Mean

Author:
Published in:
Human Language Technology Conference (HLTCon), 16-18 March 2021.

Summary

Misinformation, disinformation, and “fake news” have been used as a means of influence for millennia, but the proliferation of the internet and social media in the 21st century has enabled nefarious campaigns to achieve unprecedented scale, speed, precision, and effectiveness. In the past few years, there has been significant recognition of the threats posed by malign influence operations to geopolitical relations, democratic institutions and processes, public health and safety, and more. At the same time, the digitization of communication offers tremendous opportunities for human language technologies (HLT) to observe, interpret, and understand this publicly available content. The ability to infer intent and impact, however, remains much more elusive.
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Summary

Misinformation, disinformation, and “fake news” have been used as a means of influence for millennia, but the proliferation of the internet and social media in the 21st century has enabled nefarious campaigns to achieve unprecedented scale, speed, precision, and effectiveness. In the past few years, there has been significant recognition...

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Speaker separation in realistic noise environments with applications to a cognitively-controlled hearing aid

Summary

Future wearable technology may provide for enhanced communication in noisy environments and for the ability to pick out a single talker of interest in a crowded room simply by the listener shifting their attentional focus. Such a system relies on two components, speaker separation and decoding the listener's attention to acoustic streams in the environment. To address the former, we present a system for joint speaker separation and noise suppression, referred to as the Binaural Enhancement via Attention Masking Network (BEAMNET). The BEAMNET system is an end-to-end neural network architecture based on self-attention. Binaural input waveforms are mapped to a joint embedding space via a learned encoder, and separate multiplicative masking mechanisms are included for noise suppression and speaker separation. Pairs of output binaural waveforms are then synthesized using learned decoders, each capturing a separated speaker while maintaining spatial cues. A key contribution of BEAMNET is that the architecture contains a separation path, an enhancement path, and an autoencoder path. This paper proposes a novel loss function which simultaneously trains these paths, so that disabling the masking mechanisms during inference causes BEAMNET to reconstruct the input speech signals. This allows dynamic control of the level of suppression applied by BEAMNET via a minimum gain level, which is not possible in other state-of-the-art approaches to end-to-end speaker separation. This paper also proposes a perceptually-motivated waveform distance measure. Using objective speech quality metrics, the proposed system is demonstrated to perform well at separating two equal-energy talkers, even in high levels of background noise. Subjective testing shows an improvement in speech intelligibility across a range of noise levels, for signals with artificially added head-related transfer functions and background noise. Finally, when used as part of an auditory attention decoder (AAD) system using existing electroencephalogram (EEG) data, BEAMNET is found to maintain the decoding accuracy achieved with ideal speaker separation, even in severe acoustic conditions. These results suggest that this enhancement system is highly effective at decoding auditory attention in realistic noise environments, and could possibly lead to improved speech perception in a cognitively controlled hearing aid.
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Summary

Future wearable technology may provide for enhanced communication in noisy environments and for the ability to pick out a single talker of interest in a crowded room simply by the listener shifting their attentional focus. Such a system relies on two components, speaker separation and decoding the listener's attention to...

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