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Found 7 papers, 4 papers with code
SOREL-20M: A Large Scale Benchmark Dataset for Malicious PE Detection
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
ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation
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.
Towards Principled Uncertainty Estimation for Deep Neural Networks
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.
A Deep Learning Approach to Fast, Format-Agnostic Detection of Malicious Web Content
Malicious web content is a serious problem on the Internet today.
Git Blame Who?: Stylistic Authorship Attribution of Small, Incomplete Source Code Fragments
We show that we can also use these calibration curves in the case that we do not have linking information and thus are forced to classify individual samples directly.
Crafting Adversarial Input Sequences for Recurrent Neural Networks
Machine learning models are frequently used to solve complex security problems, as well as to make decisions in sensitive situations like guiding autonomous vehicles or predicting financial market behaviors.
When Coding Style Survives Compilation: De-anonymizing Programmers from Executable Binaries
Many distinguishing features present in source code, e. g. variable names, are removed in the compilation process, and compiler optimization may alter the structure of a program, further obscuring features that are known to be useful in determining authorship.
Cryptography and Security
