Detection of Reverse-Shell Techniques in LOTL Offense Using Augmented Data

Keskeiset käsitteet

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.

Tiivistelmä

The content discusses the challenges of detecting LOTL offensive techniques, particularly reverse shells, and proposes an augmentation framework to improve model performance. The study includes ablation studies on modeling components and evaluates the robustness of models against poisoning and evasion attacks.
Living-off-the-land (LOTL) offensive methodologies rely on malicious actions through chains of commands executed by legitimate applications. Threat actors often camouflage activity through obfuscation, making them difficult to detect without incurring false alarms.
To enhance model performance in detecting LOTL malicious activity inside legitimate logs, the authors propose an augmentation framework guided by threat intelligence.
An extensive ablation study was conducted to understand which models better handle the augmented dataset, mimicking real-world scenarios.
Results suggest that augmentation is crucial for maintaining high-predictive capabilities and robustness against attacks.
Models like Gradient Boosting Decision Trees (GBDT) can rapidly discriminate between legitimate and illicit activities in almost real-time settings.
Adversarial training can be employed to mitigate the efficacy of adversarial attacks on ML models.

Tilastot

The large enterprise network generates 2.5×105 unique command-lines within a two-hour window.
A corpus of 10k samples collected for Natural Language Processing of bash commands is available as a dataset.
Models like GBDT show high predictive capabilities with almost zero false alarms.

Lainaukset

"We propose an augmentation framework to enhance and diversify the presence of LOTL malicious activity inside legitimate logs."
"Our results suggest that augmentation is needed to maintain high-predictive capabilities."

Tärkeimmät oivallukset

Living-off-The-Land Reverse-Shell Detection by Informed Data Augmentation

by Dmitrijs Tri... klo arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.18329.pdf

Syvällisempiä Kysymyksiä

How effective are signature-based heuristic approaches compared to machine learning models

Signature-based heuristic approaches are effective in detecting common techniques without producing false positives. They can emphasize severely low False Positive Rates (FPRs) and achieve high precision in identifying specific known patterns of malicious activity. However, they may not be robust against new variants or evolving threats as they rely on predefined rules and patterns. In contrast, machine learning models have the potential to adapt and learn from data, making them more versatile in detecting novel threats and variations that may not be captured by signatures alone. Machine learning models can provide higher accuracy rates and better performance when trained on diverse datasets that encompass a wide range of behaviors.

What are the implications of poisoning attacks on model performance

Poisoning attacks can significantly impact model performance by introducing biased or misleading information into the training data. When poisoned samples are included in the training set, it can lead to a decrease in detection accuracy and an increase in false positives during inference. The presence of poisoned data can alter the decision boundaries of the model, causing it to misclassify legitimate instances or fail to detect actual threats effectively. This manipulation compromises the integrity of the model's learned representations and undermines its ability to generalize well to unseen data.

How can adversarial training be improved to enhance model robustness against evasion attacks

Adversarial training can be improved to enhance model robustness against evasion attacks by incorporating a more diverse set of adversarial perturbations during training. By exposing the model to various evasion techniques at different levels of intensity, it can learn to recognize subtle manipulations and develop defenses against them proactively. Additionally, leveraging ensemble methods that combine multiple models trained with different adversarial strategies can strengthen overall resilience against evasion tactics. Regularly updating adversarial training datasets with new attack vectors ensures continuous adaptation and readiness for emerging threats.