Publications
Exploiting temporal vulnerabilities for unauthorized access in intent-based networking
Summary
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...
Security challenges of intent-based networking
Summary
Summary
Intent-based networking (IBN) offers advantages and opportunities compared with SDN, but IBN also poses new and unique security challenges that must be overcome.
Securing the satellite software stack
Summary
Summary
Satellites and the services enabled by them, like GPS, real-time world-wide imaging, weather tracking, and worldwide communication, play an increasingly important role in modern life. To support these services satellite software is becoming increasingly complex and connected. As a result, concerns about its security are becoming prevalent. While the focus...
A deep learning-based velocity dealiasing algorithm derived from the WSR-88D open radar product generator
Summary
Summary
Radial velocity estimates provided by Doppler weather radar are critical measurements used by operational forecasters for the detection and monitoring of life-impacting storms. The sampling methods used to produce these measurements are inherently susceptible to aliasing, which produces ambiguous velocity values in regions with high winds and needs to be...
Poisoning network flow classifiers [e-print]
Summary
Summary
As machine learning (ML) classifiers increasingly oversee the automated monitoring of network traffic, studying their resilience against adversarial attacks becomes critical. This paper focuses on poisoning attacks, specifically backdoor attacks, against network traffic flow classifiers. We investigate the challenging scenario of clean-label poisoning where the adversary's capabilities are constrained to...
Improving long-text authorship verification via model selection and data tuning
Summary
Summary
Authorship verification is used to link texts written by the same author without needing a model per author, making it useful for deanonymizing users spreading text with malicious intent. Recent advances in Transformer-based language models hold great promise for author verification, though short context lengths and non-diverse training regimes present...
Holding the high ground: Defending satellites from cyber attack
Summary
Summary
MIT Lincoln Laboratory and the Space Cyber-Resiliency group at Air Force Research Laboratory-Space Vehicles Directorate have prototyped a practical, operationally capable and secure-by-design spaceflight software platform called Cyber-Hardened Satellite Software (CHSS) for building space mission applications with security, recoverability and performance as first-class system design priorities. Following a successful evaluation...
Automated exposure notification for COVID-19
Summary
Summary
Private Automated Contact Tracing (PACT) was a collaborative team and effort formed during the beginning of the Coronavirus Disease 2019 (COVID-19) pandemic. PACT's mission was to enhance contact tracing in pandemic response by designing exposure-detection functions in personal digital communication devices that have maximal public health utility while preserving privacy...
A generative approach to condition-aware score calibration for speaker verification
Summary
Summary
In speaker verification, score calibration is employed to transform verification scores to log-likelihood ratios (LLRs) which are statistically interpretable. Conventional calibration techniques apply a global score transform. However, in condition-aware (CA) calibration, information conveying signal conditions is provided as input, allowing calibration to be adaptive. This paper explores a generative...
Backdoor poisoning of encrypted traffic classifiers
Summary
Summary
Significant recent research has focused on applying deep neural network models to the problem of network traffic classification. At the same time, much has been written about the vulnerability of deep neural networks to adversarial inputs, both during training and inference. In this work, we consider launching backdoor poisoning attacks...