The authors propose an augmentation framework to enhance the detection of living-off-the-land (LOTL) reverse-shell techniques by injecting attack templates into legitimate logs, resulting in robust models with minimal false alarms.
Data augmentation enhances reverse-shell detection models' robustness against attacks.
Living-off-the-land (LOTL) reverse-shell techniques can be effectively detected through informed data augmentation, enhancing model robustness and predictive capabilities.