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Found 8 papers, 1 papers with code
NEURAL MALWARE CONTROL WITH DEEP REINFORCEMENT LEARNING
Antimalware products are a key component in detecting malware attacks, and their engines typically execute unknown programs in a sandbox prior to running them on the native operating system.
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models
Accurate and interpretable user satisfaction estimation (USE) is critical for understanding, evaluating, and continuously improving conversational systems.
AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks
Large language models (LLMs) have demonstrated impressive results on natural language tasks, and security researchers are beginning to employ them in both offensive and defensive systems.
HetTree: Heterogeneous Tree Graph Neural Network
In this paper, we present HetTree, a novel heterogeneous tree graph neural network that models both the graph structure and heterogeneous aspects in a scalable and effective manner.
Radial Spike and Slab Bayesian Neural Networks for Sparse Data in Ransomware Attacks
While traditional deep learning models have been able to achieve state-of-the-art results in a wide variety of domains, Bayesian Neural Networks, which are a class of probabilistic models, are better suited to the issues of the ransomware data.
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.
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.
