Search Results for author: Cynthia Matuszek

Found 20 papers, 4 papers with code

Living off the Analyst: Harvesting Features from Yara Rules for Malware Detection

no code implementations27 Nov 2024 Siddhant Gupta, Fred Lu, Andrew Barlow, Edward Raff, Francis Ferraro, Cynthia Matuszek, Charles Nicholas, James Holt

A strategy used by malicious actors is to "live off the land," where benign systems and tools already available on a victim's systems are used and repurposed for the malicious actor's intent.

Malware Detection

Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

1 code implementation26 Jul 2024 Ryan Barron, Maksim E. Eren, Manish Bhattarai, Ismael Boureima, Cynthia Matuszek, Boian S. Alexandrov

In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal k and the accuracy of the Binary Bleed in identifying the correct k for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores.

Dimensionality Reduction Distributed Computing +1

Small Effect Sizes in Malware Detection? Make Harder Train/Test Splits!

no code implementations25 Dec 2023 Tirth Patel, Fred Lu, Edward Raff, Charles Nicholas, Cynthia Matuszek, James Holt

Industry practitioners care about small improvements in malware detection accuracy because their models are deployed to hundreds of millions of machines, meaning a 0. 1\% change can cause an overwhelming number of false positives.

Malware Detection

Measuring Equality in Machine Learning Security Defenses: A Case Study in Speech Recognition

no code implementations17 Feb 2023 Luke E. Richards, Edward Raff, Cynthia Matuszek

Over the past decade, the machine learning security community has developed a myriad of defenses for evasion attacks.

Adversarial Robustness Fairness +2

Adversarial Transfer Attacks With Unknown Data and Class Overlap

no code implementations23 Sep 2021 Luke E. Richards, André Nguyen, Ryan Capps, Steven Forsythe, Cynthia Matuszek, Edward Raff

In this work we note that as studied, current transfer attack research has an unrealistic advantage for the attacker: the attacker has the exact same training data as the victim.

Neural Variational Learning for Grounded Language Acquisition

no code implementations20 Jul 2021 Nisha Pillai, Cynthia Matuszek, Francis Ferraro

We propose a learning system in which language is grounded in visual percepts without specific pre-defined categories of terms.

Language Acquisition

Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery

2 code implementations15 Jun 2021 John Boutsikas, Maksim E. Eren, Charles Varga, Edward Raff, Cynthia Matuszek, Charles Nicholas

The use of Machine Learning has become a significant part of malware detection efforts due to the influx of new malware, an ever changing threat landscape, and the ability of Machine Learning methods to discover meaningful distinctions between malicious and benign software.

BIG-bench Machine Learning Malware Analysis +1

Sampling Approach Matters: Active Learning for Robotic Language Acquisition

no code implementations16 Nov 2020 Nisha Pillai, Edward Raff, Francis Ferraro, Cynthia Matuszek

Ordering the selection of training data using active learning can lead to improvements in learning efficiently from smaller corpora.

Active Learning Diversity +2

Practical Cross-modal Manifold Alignment for Grounded Language

no code implementations1 Sep 2020 Andre T. Nguyen, Luke E. Richards, Gaoussou Youssouf Kebe, Edward Raff, Kasra Darvish, Frank Ferraro, Cynthia Matuszek

We propose a cross-modality manifold alignment procedure that leverages triplet loss to jointly learn consistent, multi-modal embeddings of language-based concepts of real-world items.

Grounded language learning Triplet

Presentation and Analysis of a Multimodal Dataset for Grounded Language Learning

no code implementations29 Jul 2020 Patrick Jenkins, Rishabh Sachdeva, Gaoussou Youssouf Kebe, Padraig Higgins, Kasra Darvish, Edward Raff, Don Engel, John Winder, Francis Ferraro, Cynthia Matuszek

Grounded language acquisition -- learning how language-based interactions refer to the world around them -- is amajor area of research in robotics, NLP, and HCI.

Grounded language learning

Planning with Abstract Learned Models While Learning Transferable Subtasks

no code implementations16 Dec 2019 John Winder, Stephanie Milani, Matthew Landen, Erebus Oh, Shane Parr, Shawn Squire, Marie desJardins, Cynthia Matuszek

We introduce an algorithm for model-based hierarchical reinforcement learning to acquire self-contained transition and reward models suitable for probabilistic planning at multiple levels of abstraction.

Hierarchical Reinforcement Learning reinforcement-learning +2

A Joint Model of Language and Perception for Grounded Attribute Learning

no code implementations27 Jun 2012 Cynthia Matuszek, Nicholas FitzGerald, Luke Zettlemoyer, Liefeng Bo, Dieter Fox

As robots become more ubiquitous and capable, it becomes ever more important to enable untrained users to easily interact with them.

Attribute Language Modeling +1

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