Adversarial PDU Training Using Header Field Perturbations
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Solution Overview
Problem
ML models trained to detect malicious protocol data units (PDUs) are vulnerable to reordering attacks and unknown biases in training data, leading to evasion by cyber attackers, particularly in high-volume command and control (C2) attacks.
Innovation Solution
An adversarial training data generator perturbs header fields in PDUs through reordering, replacement, and grid search to create robust training data, up-sampling, and optimize models to strengthen detection against such attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ML models are trained on conventional training data without adversarial perturbations, then the training process is simple and fast, but the models become vulnerable to reordering attacks and evasion by cyber attackers
Solution Approach 1:
The system performs preliminary adversarial perturbations on training data before model training begins. The training data generator applies reordering, replacement, and grid search transformations to create adversarial training examples in advance, so that when the ML model is trained on this pre-perturbed data, it learns to detect malicious PDUs despite header field reordering attacks. This preliminary action ensures the model is robust from the start without requiring complex real-time adjustments during training.
Solution Approach 2:
The system implements feedback mechanisms where the training data generator uses information about model performance and attack effectiveness to iteratively improve the adversarial training data. The generator receives feedback about which perturbations are most effective at evading detection and adjusts the training data generation strategy accordingly, creating a feedback loop that continuously improves detection reliability while managing training complexity through automated optimization.
2Measurement precision
If the training data is up-sampled and adversarially perturbed to increase robustness, then detection accuracy improves, but the training time and computational resources increase
Solution Approach 1:
The system dynamically adjusts training parameters including the degree of perturbation, the number of adversarial examples generated per original sample, and the sampling rates for different types of PDUs. The training data generator modifies these parameters based on the specific characteristics of the dataset and the current training epoch, allowing the system to optimize the balance between detection accuracy and training time by changing parameters rather than using fixed rigid approaches.
Solution Approach 2:
Instead of applying maximum possible adversarial perturbations to all training data, the system applies partial actions selectively. The training data generator determines the optimal level of perturbation strength and applies it only to the most critical training samples. This partial action approach maintains sufficient detection accuracy while significantly reducing the computational burden compared to exhaustive adversarial training.
3Adaptability or versatility
If header fields are perturbed through reordering and replacement to simulate attacks, then the model becomes more resilient to evasion, but the training data becomes more complex and harder to process
Solution Approach 1:
The system segments the header field perturbation process into distinct operations: reordering operations that rearrange field sequences, replacement operations that substitute field values, and grid search operations that systematically explore value combinations. The training data generator applies these segmentation steps in a controlled sequence, managing the complexity by breaking down the overall perturbation task into manageable, discrete transformations that can be processed efficiently.
Solution Approach 2:
The training data generator is designed as a universal module that handles multiple types of perturbations (reordering, replacement, grid search) through a single integrated framework. This multi-functional design allows the same generator to create various types of adversarial examples without requiring separate processing pipelines, thereby managing training data complexity while maintaining high model adaptability to different attack vectors.
Data Source
AI summary
A modular adversarial training data generator (“generator”) generates adversarial training data for a machine learning (ML) model to detect malicious protocol data units (PDUs). The adversarial training data mimics high volume cyberattacks by perturbing PDUs to bypass malicious detection systems. For Hypertext Transfer Protocol (HTTP) PDUs, the generator rearranged, replaces, and grid searches values of HTTP header fields to generate the adversarial training data. The generator further biases grid search based on metrics for values of HTTP header fields that quantify impact of replacing the values on malicious verdicts by ML models trained on the adversarial training data.