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Joshua Saxe
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2020 – today
- 2023
- [i10]Manish Bhatt, Sahana Chennabasappa, Cyrus Nikolaidis, Shengye Wan, Ivan Evtimov, Dominik Gabi, Daniel Song, Faizan Ahmad, Cornelius Aschermann, Lorenzo Fontana, Sasha Frolov, Ravi Prakash Giri, Dhaval Kapil, Yiannis Kozyrakis, David LeBlanc, James Milazzo, Aleksandar Straumann, Gabriel Synnaeve, Varun Vontimitta, Spencer Whitman, Joshua Saxe:
Purple Llama CyberSecEval: A Secure Coding Benchmark for Language Models. CoRR abs/2312.04724 (2023) - 2020
- [i9]Younghoo Lee, Joshua Saxe, Richard E. Harang:
CATBERT: Context-Aware Tiny BERT for Detecting Social Engineering Emails. CoRR abs/2010.03484 (2020)
2010 – 2019
- 2019
- [i8]Ben Gelman, Bryan Hoyle, Jessica Moore, Joshua Saxe, David Slater:
A Language-Agnostic Model for Semantic Source Code Labeling. CoRR abs/1906.01032 (2019) - 2018
- [c10]Ben Gelman, Bryan Hoyle, Jessica Moore, Joshua Saxe, David Slater:
A language-agnostic model for semantic source code labeling. MASES@ASE 2018: 36-44 - [c9]Joshua Saxe, Richard E. Harang, Cody Wild, Hillary Sanders:
A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content. IEEE Symposium on Security and Privacy Workshops 2018: 8-14 - [i7]Joshua Saxe, Richard E. Harang, Cody Wild, Hillary Sanders:
A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content. CoRR abs/1804.05020 (2018) - [i6]Ethan M. Rudd, Richard E. Harang, Joshua Saxe:
MEADE: Towards a Malicious Email Attachment Detection Engine. CoRR abs/1804.08162 (2018) - 2017
- [i5]Joshua Saxe, Konstantin Berlin:
eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys. CoRR abs/1702.08568 (2017) - 2016
- [i4]Joshua Saxe, Rafael Turner, Kristina Blokhin:
CrowdSource: Automated Inference of High Level Malware Functionality from Low-Level Symbols Using a Crowd Trained Machine Learning Model. CoRR abs/1605.08642 (2016) - [i3]Konstantin Berlin, Joshua Saxe:
Improving Zero-Day Malware Testing Methodology Using Statistically Significant Time-Lagged Test Samples. CoRR abs/1608.00669 (2016) - 2015
- [c8]Konstantin Berlin, David Slater, Joshua Saxe:
Malicious Behavior Detection using Windows Audit Logs. AISec@CCS 2015: 35-44 - [c7]Joshua Saxe, Konstantin Berlin:
Deep neural network based malware detection using two dimensional binary program features. MALWARE 2015: 11-20 - [i2]Konstantin Berlin, David Slater, Joshua Saxe:
Malicious Behavior Detection using Windows Audit Logs. CoRR abs/1506.04200 (2015) - [i1]Joshua Saxe, Konstantin Berlin:
Deep Neural Network Based Malware Detection Using Two Dimensional Binary Program Features. CoRR abs/1508.03096 (2015) - 2014
- [c6]Joshua Saxe, Rafael Turner, Kristina Blokhin:
CrowdSource: Automated inference of high level malware functionality from low-level symbols using a crowd trained machine learning model. MALWARE 2014: 68-75 - [c5]Robert Gove, Joshua Saxe, Sigfried Gold, Alexander Long, Giacomo Bergamo:
SEEM: a scalable visualization for comparing multiple large sets of attributes for malware analysis. VizSEC 2014: 72-79 - [c4]Alexander Long, Joshua Saxe, Robert Gove:
Detecting malware samples with similar image sets. VizSEC 2014: 88-95 - 2013
- [c3]Joshua Saxe, David Mentis, Christopher Greamo:
Mining Web Technical Discussions to Identify Malware Capabilities. ICDCS Workshops 2013: 1-5 - [c2]Kristina Blokhin, Joshua Saxe, David Mentis:
Malware Similarity Identification Using Call Graph Based System Call Subsequence Features. ICDCS Workshops 2013: 6-10 - 2012
- [c1]Joshua Saxe, David Mentis, Christopher Greamo:
Visualization of shared system call sequence relationships in large malware corpora. VizSEC 2012: 33-40
Coauthor Index
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