Adversarial Defense and Detection Framework for Encrypted Network Traffic Classification

A Comprehensive Review on Adversarial Attacks and Detection Techniques in Deep Learning Models for Image Analysis

Enhancing robustness of deep learning-based malware detection against adversarial attacks

A Two-Stage Adversarial Defense Architecture for Robust Fraud Detection on Imbalanced Financial Data

: As artificial intelligence becomes increasingly embedded in financial systems, ensuring the security and robustness of these models is critical, particularly in sensitive tasks like credit card fraud detection. Despite their predictive success, deep learning models remain vulnerable to adversarial examples: subtly manipulated inputs that can mislead classification outcomes. Unlike existing approaches that typically rely on either adversarial training or standalone input filtering, this paper proposes a unified dual-defense framework that jointly integrates adversarial training with a denoising autoencoder (DAE)-based filtering mechanism, specifically designed for imbalanced tabular financial data under adversarial conditions. Using a real-world, imbalanced credit card transaction dataset of 284,807 transactions, the proposed method achieves superior performance on clean data with an accuracy of 0.991, F1-score of 0.872, and Area Under the Precision–Recall Curve (AUC-PR) of 0.952. Under adversarial conditions, the framework maintains robustness, achieving an F1-score of 0.648 against Fast Gradient Sign Method (FGSM) and 0.610 against Projected Gradient Descent (PGD) attacks, outperforming baseline models by margins of > 0.10 in F1. In contrast to prior work that primarily focuses on predictive performance or single-defense strategies, the proposed approach explicitly targets adversarial robustness in financial fraud detection through a complementary integration of defense mechanisms. Ablation studies confirm the complementary effect of adversarial training and DAE-based filtering, while detection analysis shows an adversarial detection accuracy of 87.8%. These findings highlight the practicality of hybrid defense strategies for improving the trustworthiness of AI systems in finance.

An advanced framework for malware detection using adversarial learning

Detecting adversarial evasion in deep learning intrusion detection systems using explainable AI

Deep learning based network intrusion detection systems (IDS) can achieve strong traffic classification performance, but their resilience to adversarial manipulation remains a critical concern. This study evaluates the adversarial robustness of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models in a multiclass intrusion detection setting using the Train_Test_Network dataset with ten traffic classes. The models were trained on true sliding flow-window sequences under a unified preprocessing pipeline to support fair comparison. Adversarial robustness was first assessed under a white-box Fast Gradient Sign Method (FGSM) setting and then broadened through additional FGSM and Projected Gradient Descent (PGD) stress testing. SHapley Additive exPlanations (SHAP) were further used to analyse explanation instability under clean and adversarial conditions, and explanation-drift features were evaluated as a secondary adversarial detection signal. Under clean evaluation, both models achieved strong and nearly identical performance, with accuracies of 0.9614 for LSTM and 0.9615 for GRU and weighted F1-scores of 0.9597 and 0.9598, respectively. Under the main FGSM condition, performance declined substantially: the LSTM achieved adversarial accuracy of 0.6094 and weighted F1-score of 0.6290 with an evasion rate of 37.38%, while the GRU achieved adversarial accuracy of 0.5130 and weighted F1-score of 0.5690 with an evasion rate of 47.02%. The broader robustness sweep showed that iterative PGD exposed stronger fragility than FGSM alone. SHAP analysis indicated that adversarial perturbation altered both prediction outcomes and local explanation structure. A learned explanation-driven detector improved over the rule-based baseline, while larger-scale validation confirmed that explanation drift remained informative, though not perfectly separable, at broader scale. Overall, the results show that strong clean performance does not imply adversarial robustness, and that explanation drift provides a useful auxiliary signal for adversarial monitoring in recurrent IDS models.

Evaluating need for adversarial training data given algorithmic defense methods against adversarial attacks

An adversarial attack is a modification to the pixels of an image for the purpose of making a machine learning system misclassify the image. The foremost defense against adversarial attacks is adversarial training: a process in which the machine learning system trains on the already attacked images. But, this is not the only kind of defense. There are also algorithmic defense methods, which work to modify the learning process to be resilient to adversarial attacks without involving attacked examples. In this study, we considered three algorithmic defense settings: no algorithmic defense, defensive distillation, and gradient masking. Then we evaluated the role of adversarial training as part of defending machine learning models from adversarial attacks. Specifically, we set up a baseline image classifier for images of digits (MNIST dataset) and attacked the images using the fast gradient sign method. We hypothesized that introducing adversarial training for this classifier would significantly improve downstream classification accuracy in all three algorithmic defense settings. We found that, for all algorithmic defense settings applied to neural networks with between one and six convolutional layers, adding adversarial training consistently resulted in a statistically significant increase in accuracy. While these findings are limited by the specific data, parameters, and algorithms explored, our results suggest that implementing adversarial training within all lines of defense against adversarial attacks would be beneficial. We believe that this insight increases awareness of cybersecurity threats such as adversarial attacks and lowers the barrier of entry to protect against them.