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pro vyhledávání: '"Rudd, P. M."'
Brain Computer Interface (BCI) technologies have the potential to improve the lives of millions of people around the world, whether through assistive technologies or clinical diagnostic tools. Despite advancements in the field, however, at present co
Externí odkaz:
http://arxiv.org/abs/2301.12322
Publikováno v:
Proceedings of the 1st Workshop on Robust Malware Analysis (2022) 21-31
In this paper, we assess the viability of transformer models in end-to-end InfoSec settings, in which no intermediate feature representations or processing steps occur outside the model. We implement transformer models for two distinct InfoSec data f
Externí odkaz:
http://arxiv.org/abs/2212.02666
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. Speci
Externí odkaz:
http://arxiv.org/abs/2212.02663
Autor:
Harang, Richard, Rudd, Ethan M.
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 ab
Externí odkaz:
http://arxiv.org/abs/2012.07634
Autor:
Rudd, Ethan M., Abdallah, Ahmed
Machine Learning (ML) for information security (InfoSec) utilizes distinct data types and formats which require different treatments during optimization/training on raw data. In this paper, we implement a malicious/benign URL predictor based on a tra
Externí odkaz:
http://arxiv.org/abs/2011.03040
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Akademický článek
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Machine learning (ML) used for static portable executable (PE) malware detection typically employs per-file numerical feature vector representations as input with one or more target labels during training. However, there is much orthogonal informatio
Externí odkaz:
http://arxiv.org/abs/1905.06987
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. Alth
Externí odkaz:
http://arxiv.org/abs/1905.06262
Malware detection is a popular application of Machine Learning for Information Security (ML-Sec), in which an ML classifier is trained to predict whether a given file is malware or benignware. Parameters of this classifier are typically optimized suc
Externí odkaz:
http://arxiv.org/abs/1903.05700