| Deep neural network based malware detection using two dimensional binary program features J Saxe, K Berlin 2015 10th international conference on malicious and unwanted software …, 2015 | 879 | 2015 |
| eXpose: A character-level convolutional neural network with embeddings for detecting malicious URLs, file paths and registry keys J Saxe, K Berlin arXiv preprint arXiv:1702.08568, 2017 | 288 | 2017 |
| Methods and apparatus for machine learning based malware detection JD Saxe, K Berlin US Patent 9,690,938, 2017 | 128 | 2017 |
| Malicious behavior detection using windows audit logs K Berlin, D Slater, J Saxe Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security …, 2015 | 125 | 2015 |
| Malware data science: attack detection and attribution J Saxe, H Sanders No Starch Press, 2018 | 78 | 2018 |
| Visualization of shared system call sequence relationships in large malware corpora J Saxe, D Mentis, C Greamo Proceedings of the ninth international symposium on visualization for cyber …, 2012 | 77 | 2012 |
| SeqDroid: Obfuscated Android malware detection using stacked convolutional and recurrent neural networks WY Lee, J Saxe, R Harang Deep learning applications for cyber security, 197-210, 2019 | 68 | 2019 |
| Methods and apparatus for detection of malicious documents using machine learning JD Saxe, R HARANG US Patent 11,003,774, 2021 | 59 | 2021 |
| The llama 3 herd of models A Dubey, A Jauhri, A Pandey, A Kadian, A Al-Dahle, A Letman, A Mathur, ... arXiv preprint arXiv:2407.21783, 2024 | 53 | 2024 |
| Methods and apparatus for detecting whether a string of characters represents malicious activity using machine learning JD Saxe US Patent 10,318,735, 2019 | 48 | 2019 |
| A deep learning approach to fast, format-agnostic detection of malicious web content J Saxe, R Harang, C Wild, H Sanders 2018 IEEE Security and Privacy Workshops (SPW), 8-14, 2018 | 46 | 2018 |
| Meade: Towards a malicious email attachment detection engine EM Rudd, R Harang, J Saxe 2018 IEEE International Symposium on Technologies for Homeland Security (HST …, 2018 | 41 | 2018 |
| SEEM: a scalable visualization for comparing multiple large sets of attributes for malware analysis R Gove, J Saxe, S Gold, A Long, G Bergamo Proceedings of the Eleventh Workshop on Visualization for Cyber Security, 72-79, 2014 | 41 | 2014 |
| Computer augmented threat evaluation JD Saxe, AJ Thomas, R Humphries, SN Reed, KD Ray, JH Levy US Patent 10,938,838, 2021 | 40 | 2021 |
| CATBERT: Context-aware tiny BERT for detecting social engineering emails Y Lee, J Saxe, R Harang arXiv preprint arXiv:2010.03484, 2020 | 40 | 2020 |
| Methods and apparatus for machine learning based malware detection JD Saxe, K Berlin US Patent 9,910,986, 2018 | 37 | 2018 |
| Malware similarity identification using call graph based system call subsequence features K Blokhin, J Saxe, D Mentis 2013 IEEE 33rd International Conference on Distributed Computing Systems …, 2013 | 33 | 2013 |
| Methods and apparatus for using machine learning on multiple file fragments to identify malware JD Saxe, R Harang US Patent 10,635,813, 2020 | 31 | 2020 |
| Purple llama cyberseceval: A secure coding benchmark for language models M Bhatt, S Chennabasappa, C Nikolaidis, S Wan, I Evtimov, D Gabi, ... arXiv preprint arXiv:2312.04724, 2023 | 29 | 2023 |
| CrowdSource: Automated inference of high level malware functionality from low-level symbols using a crowd trained machine learning model J Saxe, R Turner, K Blokhin 2014 9th International Conference on Malicious and Unwanted Software: The …, 2014 | 28 | 2014 |
