Publications
Practical principle of least privilege for secure embedded systems
Summary
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...
A cybersecurity moonshot
Summary
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.
PATHATTACK: attacking shortest paths in complex networks
Summary
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...
Health-informed policy gradients for multi-agent reinforcement learning
Summary
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...
Combating Misinformation: HLT Highlights from MIT Lincoln Laboratory
Summary
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...
Combating Misinformation: What HLT Can (and Can't) Do When Words Don't Say What They Mean
Summary
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...
Speaker separation in realistic noise environments with applications to a cognitively-controlled hearing aid
Summary
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...
More than a fair share: Network Data Remanence attacks against secret sharing-based schemes
Summary
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...
Beyond expertise and roles: a framework to characterize the stakeholders of interpretable machine learning and their needs
Summary
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...
Seasonal Inhomogeneous Nonconsecutive Arrival Process Search and Evaluation
Summary
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...