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Found 7 papers, 3 papers with code
ScriptNet: Neural Static Analysis for Malicious JavaScript Detection
We use the Convoluted Partitioning of Long Sequences (CPoLS) model, which processes Javascript files as byte sequences.
Learning Edge Properties in Graphs from Path Aggregations
Graph edges, along with their labels, can represent information of fundamental importance, such as links between web pages, friendship between users, the rating given by users to other users or items, and much more.
A New Family of Neural Networks Provably Resistant to Adversarial Attacks
The provable accuracy of MWD networks is superior even to the observed accuracy of ReLU networks trained with the help of adversarial examples.
Robust Neural Malware Detection Models for Emulation Sequence Learning
These models target the core of the malicious operation by learning the presence and pattern of co-occurrence of malicious event actions from within these sequences.
Neural Classification of Malicious Scripts: A study with JavaScript and VBScript
LaMP and CPoLS yield a TPR of 69. 3% and 67. 9%, respectively, at an FPR of 1. 0% on a collection of 240, 504 VBScript files.
Learning User Intent from Action Sequences on Interactive Systems
In this paper, we present models to optimize interactive systems by learning and analyzing user intent through their actions on the system.
Learning From Graph Neighborhoods Using LSTMs
Many prediction problems can be phrased as inferences over local neighborhoods of graphs.
