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Found 8 papers, 1 papers with code
Being Single Has Benefits. Instance Poisoning to Deceive Malware Classifiers
The performance of a machine learning-based malware classifier depends on the large and updated training set used to induce its model.
FOOD: Fast Out-Of-Distribution Detector
Deep neural networks (DNNs) perform well at classifying inputs associated with the classes they have been trained on, which are known as in distribution inputs.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
GIM: Gaussian Isolation Machines
In many cases, neural network classifiers are likely to be exposed to input data that is outside of their training distribution data.
End-to-End Deep Neural Networks and Transfer Learning for Automatic Analysis of Nation-State Malware
We record the dynamic behavior of the APT when run in a sandbox and use it as raw input for the neural network, allowing the DNN to learn high level feature abstractions of the APTs itself.
Defense Methods Against Adversarial Examples for Recurrent Neural Networks
Using our methods we were able to decrease the effectiveness of such attack from 99. 9% to 15%.
Cryptography and Security
Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers
In this paper, we present a generic, query-efficient black-box attack against API call-based machine learning malware classifiers.
DeepAPT: Nation-State APT Attribution Using End-to-End Deep Neural Networks
The task of attributing an APT to a specific nation-state is extremely challenging for several reasons.
Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers
In this paper, we present a black-box attack against API call based machine learning malware classifiers, focusing on generating adversarial sequences combining API calls and static features (e. g., printable strings) that will be misclassified by the classifier without affecting the malware functionality.
