Publications

Refine Results

(Filters Applied) Clear All

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.
READ LESS

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...

READ MORE

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.
READ LESS

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.

READ MORE

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.
READ LESS

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...

READ MORE

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.
READ LESS

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...

READ MORE

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
READ LESS

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...

READ MORE

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.
READ LESS

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...

READ MORE

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.
READ LESS

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...

READ MORE

More than a fair share: Network Data Remanence attacks against secret sharing-based schemes

Published in:
Network and Distributed Systems Security Symp., NDSS, 23-26 February 2021.

Summary

With progress toward a practical quantum computer has come an increasingly rapid search for quantum-safe, secure communication schemes that do not rely on discrete logarithm or factorization problems. One such encryption scheme, Multi-path Switching with Secret Sharing (MSSS), combines secret sharing with multi-path switching to achieve security as long as the adversary does not have global observability of all paths and thus cannot capture enough shares to reconstruct messages. MSSS assumes that sending a share on a path is an atomic operation and all paths have the same delay. In this paper, we identify a side-channel vulnerability for MSSS, created by the fact that in real networks, sending a share is not an atomic operation as paths have multiple hops and different delays. This channel, referred to as Network Data Remanence (NDR), is present in all schemes like MSSS whose security relies on transfer atomicity and all paths having same delay. We demonstrate the presence of NDR in a physical testbed. We then identify two new attacks that aim to exploit the side channel, referred to as NDR Blind and NDR Planned, propose an analytical model to analyze the attacks, and demonstrate them using an implementation of MSSS based on the ONOS SDN controller. Finally, we present a countermeasure for the attacks and show its effectiveness in simulations and Mininet experiments.
READ LESS

Summary

With progress toward a practical quantum computer has come an increasingly rapid search for quantum-safe, secure communication schemes that do not rely on discrete logarithm or factorization problems. One such encryption scheme, Multi-path Switching with Secret Sharing (MSSS), combines secret sharing with multi-path switching to achieve security as long as...

READ MORE

Beyond expertise and roles: a framework to characterize the stakeholders of interpretable machine learning and their needs

Published in:
Proc. Conf. on Human Factors in Computing Systems, 8-13 May 2021, article no. 74.

Summary

To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them. In this paper, we eschew prior expertise- and role-based categorizations of interpretability stakeholders in favor of a more granular framework that decouples stakeholders' knowledge from their interpretability needs. We characterize stakeholders by their formal, instrumental, and personal knowledge and how it manifests in the contexts of machine learning, the data domain, and the general milieu. We additionally distill a hierarchical typology of stakeholder needs that distinguishes higher-level domain goals from lower-level interpretability tasks. In assessing the descriptive, evaluative, and generative powers of our framework, we find our more nuanced treatment of stakeholders reveals gaps and opportunities in the interpretability literature, adds precision to the design and comparison of user studies, and facilitates a more reflexive approach to conducting this research.
READ LESS

Summary

To ensure accountability and mitigate harm, it is critical that diverse stakeholders can interrogate black-box automated systems and find information that is understandable, relevant, and useful to them. In this paper, we eschew prior expertise- and role-based categorizations of interpretability stakeholders in favor of a more granular framework that decouples...

READ MORE

Seasonal Inhomogeneous Nonconsecutive Arrival Process Search and Evaluation

Published in:
25th International Conference on Pattern Recognition [submitted]

Summary

Time series often exhibit seasonal patterns, and identification of these patterns is essential to understanding thedata and predicting future behavior. Most methods train onlarge datasets and can fail to predict far past the training data. This limitation becomes more pronounced when data is sparse. This paper presents a method to fit a model to seasonal time series data that maintains predictive power when data is limited. This method, called SINAPSE, combines statistical model fitting with an information criteria to search for disjoint, andpossibly nonconsecutive, regimes underlying the data, allowing for a sparse representation resistant to overfitting.
READ LESS

Summary

Time series often exhibit seasonal patterns, and identification of these patterns is essential to understanding thedata and predicting future behavior. Most methods train onlarge datasets and can fail to predict far past the training data. This limitation becomes more pronounced when data is sparse. This paper presents a method to...

READ MORE