Search Results for author: Ethan M. Rudd

Found 17 papers, 5 papers with code

Cross-Subject Deep Transfer Models for Evoked Potentials in Brain-Computer Interface

no code implementations29 Jan 2023 Chad Mello, Troy Weingart, Ethan M. Rudd

We then partition this dataset into a transfer learning benchmark and demonstrate that our approach significantly reduces data collection burden per-subject.

Benchmarking Brain Computer Interface +3

Efficient Malware Analysis Using Metric Embeddings

no code implementations5 Dec 2022 Ethan M. Rudd, David Krisiloff, Scott Coull, Daniel Olszewski, Edward Raff, James Holt

In this paper, we explore the use of metric learning to embed Windows PE files in a low-dimensional vector space for downstream use in a variety of applications, including malware detection, family classification, and malware attribute tagging.

Attribute Malware Analysis +2

Transformers for End-to-End InfoSec Tasks: A Feasibility Study

no code implementations5 Dec 2022 Ethan M. Rudd, Mohammad Saidur Rahman, Philip Tully

We implement transformer models for two distinct InfoSec data formats - specifically URLs and PE files - in a novel end-to-end approach, and explore a variety of architectural designs, training regimes, and experimental settings to determine the ingredients necessary for performant detection models.

Binary Classification Malware Detection

SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection

2 code implementations14 Dec 2020 Richard Harang, Ethan M. Rudd

In this paper we describe the SOREL-20M (Sophos/ReversingLabs-20 Million) dataset: a large-scale dataset consisting of nearly 20 million files with pre-extracted features and metadata, high-quality labels derived from multiple sources, information about vendor detections of the malware samples at the time of collection, and additional ``tags'' related to each malware sample to serve as additional targets.

Cryptography and Security

Training Transformers for Information Security Tasks: A Case Study on Malicious URL Prediction

1 code implementation5 Nov 2020 Ethan M. Rudd, Ahmed Abdallah

Machine Learning (ML) for information security (InfoSec) utilizes distinct data types and formats which require different treatments during optimization/training on raw data.

Learning from Context: Exploiting and Interpreting File Path Information for Better Malware Detection

1 code implementation16 May 2019 Adarsh Kyadige, Ethan M. Rudd, Konstantin Berlin

In this paper, we propose utilizing a static source of contextual information -- the path of the PE file -- as an auxiliary input to the classifier.

Malware Detection

Automatic Malware Description via Attribute Tagging and Similarity Embedding

2 code implementations15 May 2019 Felipe N. Ducau, Ethan M. Rudd, Tad M. Heppner, Alex Long, Konstantin Berlin

With the rapid proliferation and increased sophistication of malicious software (malware), detection methods no longer rely only on manually generated signatures but have also incorporated more general approaches like machine learning detection.

Attribute BIG-bench Machine Learning +2

ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation

1 code implementation13 Mar 2019 Ethan M. Rudd, Felipe N. Ducau, Cody Wild, Konstantin Berlin, Richard Harang

In this work, we fit deep neural networks to multiple additional targets derived from metadata in a threat intelligence feed for Portable Executable (PE) malware and benignware, including a multi-source malicious/benign loss, a count loss on multi-source detections, and a semantic malware attribute tag loss.

Attribute Malware Detection +1

Towards Principled Uncertainty Estimation for Deep Neural Networks

no code implementations29 Oct 2018 Richard Harang, Ethan M. Rudd

When the cost of misclassifying a sample is high, it is useful to have an accurate estimate of uncertainty in the prediction for that sample.

Bayesian Inference

Facial Attributes: Accuracy and Adversarial Robustness

no code implementations4 Jan 2018 Andras Rozsa, Manuel Günther, Ethan M. Rudd, Terrance E. Boult

Facial attributes, emerging soft biometrics, must be automatically and reliably extracted from images in order to be usable in stand-alone systems.

Adversarial Robustness Attribute +1

Toward Open-Set Face Recognition

no code implementations3 May 2017 Manuel Günther, Steve Cruz, Ethan M. Rudd, Terrance E. Boult

In this paper, we address the widespread misconception that thresholding verification-like scores is a good way to solve the open-set face identification problem, by formulating an open-set face identification protocol and evaluating different strategies for assessing similarity.

Face Identification Face Recognition +2

Automated Big Text Security Classification

no code implementations21 Oct 2016 Khudran Alzhrani, Ethan M. Rudd, Terrance E. Boult, C. Edward Chow

To analyze the ACESS system, we constructed a novel dataset, containing formerly classified paragraphs from diplomatic cables made public by the WikiLeaks organization.

Classification General Classification

Are Facial Attributes Adversarially Robust?

no code implementations18 May 2016 Andras Rozsa, Manuel Günther, Ethan M. Rudd, Terrance E. Boult

We show that FFA generates more adversarial examples than other related algorithms, and that DCNNs for certain attributes are generally robust to adversarial inputs, while DCNNs for other attributes are not.

Attribute Attribute Extraction +2

PARAPH: Presentation Attack Rejection by Analyzing Polarization Hypotheses

no code implementations10 May 2016 Ethan M. Rudd, Manuel Gunther, Terrance E. Boult

Thus, there is great demand for an effective and low cost system capable of rejecting such attacks. To this end we introduce PARAPH -- a novel hardware extension that exploits different measurements of light polarization to yield an image space in which presentation media are readily discernible from Bona Fide facial characteristics.

Adversarial Diversity and Hard Positive Generation

no code implementations5 May 2016 Andras Rozsa, Ethan M. Rudd, Terrance E. Boult

Finally, we demonstrate on LeNet and GoogLeNet that fine-tuning with a diverse set of hard positives improves the robustness of these networks compared to training with prior methods of generating adversarial images.

Data Augmentation

A Survey of Stealth Malware: Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions

no code implementations19 Mar 2016 Ethan M. Rudd, Andras Rozsa, Manuel Günther, Terrance E. Boult

While machine learning offers promising potential for increasingly autonomous solutions with improved generalization to new malware types, both at the network level and at the host level, our findings suggest that several flawed assumptions inherent to most recognition algorithms prevent a direct mapping between the stealth malware recognition problem and a machine learning solution.

BIG-bench Machine Learning

The Extreme Value Machine

no code implementations19 Jun 2015 Ethan M. Rudd, Lalit P. Jain, Walter J. Scheirer, Terrance E. Boult

It is often desirable to be able to recognize when inputs to a recognition function learned in a supervised manner correspond to classes unseen at training time.

Incremental Learning

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