Search Results for author:
Found 5 papers, 3 papers with code
SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines
As a result of decades of research, Windows malware detection is approached through a plethora of techniques.
Living-off-The-Land Reverse-Shell Detection by Informed Data Augmentation
The living-off-the-land (LOTL) offensive methodologies rely on the perpetration of malicious actions through chains of commands executed by legitimate applications, identifiable exclusively by analysis of system logs.
Nebula: Self-Attention for Dynamic Malware Analysis
Dynamic analysis enables detecting Windows malware by executing programs in a controlled environment, and storing their actions in log reports.
Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations
The detection heuristic in contemporary machine learning Windows malware classifiers is typically based on the static properties of the sample since dynamic analysis through virtualization is challenging for vast quantities of samples.
Shell Language Processing: Unix command parsing for Machine Learning
In this article, we present a Shell Language Preprocessing (SLP) library, which implements tokenization and encoding directed at parsing Unix and Linux shell commands.
