James Holt

A Coreset Learning Reality Check

Jan 15, 2023
Fred Lu, Edward Raff, James Holt

Figure 2 for A Coreset Learning Reality Check

Figure 3 for A Coreset Learning Reality Check

Figure 4 for A Coreset Learning Reality Check

* To appear in the Thirty-Seventh AAAI Conference on Artificial Intelligence

Access Paper or Ask Questions

Efficient Malware Analysis Using Metric Embeddings

Dec 05, 2022
Ethan M. Rudd, David Krisiloff, Scott Coull, Daniel Olszewski, Edward Raff, James Holt

Figure 2 for Efficient Malware Analysis Using Metric Embeddings

Figure 3 for Efficient Malware Analysis Using Metric Embeddings

Figure 4 for Efficient Malware Analysis Using Metric Embeddings

* Pre-print of a manuscript submitted to the ACM Digital Threats: Research and Practice (DTRAP) Special Issue on Applied Machine Learning for Information Security. 19 Pages

Access Paper or Ask Questions

Lempel-Ziv Networks

Nov 23, 2022
Rebecca Saul, Mohammad Mahmudul Alam, John Hurwitz, Edward Raff, Tim Oates, James Holt

* I Can't Believe It's Not Better Workshop at NeurIPS 2022

Access Paper or Ask Questions

Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations

Jun 13, 2022
Mohammad Mahmudul Alam, Edward Raff, Tim Oates, James Holt

Figure 2 for Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations

Figure 3 for Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations

Figure 4 for Deploying Convolutional Networks on Untrusted Platforms Using 2D Holographic Reduced Representations

* To appear in the Proceedings of the 39 th International Conference on Machine Learning, Baltimore, Maryland, USA, PMLR 162, 2022

Access Paper or Ask Questions

Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection

Jun 07, 2022
Michael D. Wong, Edward Raff, James Holt, Ravi Netravali

Figure 2 for Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection

Figure 3 for Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection

Figure 4 for Marvolo: Programmatic Data Augmentation for Practical ML-Driven Malware Detection

* 15 pages, 7 figures

Access Paper or Ask Questions

Proceedings of the Artificial Intelligence for Cyber Security (AICS) Workshop at AAAI 2022

Mar 01, 2022
James Holt, Edward Raff, Ahmad Ridley, Dennis Ross, Arunesh Sinha, Diane Staheli, William Streilen, Milind Tambe, Yevgeniy Vorobeychik, Allan Wollaber

Access Paper or Ask Questions

Out of Distribution Data Detection Using Dropout Bayesian Neural Networks

Feb 18, 2022
Andre T. Nguyen, Fred Lu, Gary Lopez Munoz, Edward Raff, Charles Nicholas, James Holt

Figure 2 for Out of Distribution Data Detection Using Dropout Bayesian Neural Networks

Figure 3 for Out of Distribution Data Detection Using Dropout Bayesian Neural Networks

Figure 4 for Out of Distribution Data Detection Using Dropout Bayesian Neural Networks

Access Paper or Ask Questions

Learning with Holographic Reduced Representations

Sep 05, 2021
Ashwinkumar Ganesan, Hang Gao, Sunil Gandhi, Edward Raff, Tim Oates, James Holt, Mark McLean

Figure 2 for Learning with Holographic Reduced Representations

Figure 3 for Learning with Holographic Reduced Representations

Figure 4 for Learning with Holographic Reduced Representations

Access Paper or Ask Questions

Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints

Aug 09, 2021
Andre T. Nguyen, Edward Raff, Charles Nicholas, James Holt

Figure 2 for Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints

Figure 3 for Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints

Figure 4 for Leveraging Uncertainty for Improved Static Malware Detection Under Extreme False Positive Constraints

Access Paper or Ask Questions

Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors

Oct 22, 2020
Edward Raff, Bobby Filar, James Holt

Figure 2 for Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors

Figure 3 for Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors

Figure 4 for Getting Passive Aggressive About False Positives: Patching Deployed Malware Detectors

* to appear in IEEE International Conference on Data Mining Workshop (ICDM) on Deep Learning for Cyber Threat Intelligence (DL-CTI)

Access Paper or Ask Questions

1

2

>>