Adversarial Attacks and Defense Mechanisms on Machine Learning Models for Cybersecurity Applications

Abstract

The Machine Learning (ML) models in cybersecurity systems is growing rapidly in areas such as intrusion detection, malware classification, and phishing URL detection. ML systems, however, are exceptionally susceptible to adversarial attacks. These attacks consist of applying carefully engineered perturbations to the input data, which forces an ML system to misclassify the input data and leads to serious violations of security. This paper proposes a hybrid defense framework to improve the ML model\'s robustness against adversarial attack. The approach uses a combination of adversarial training, feature squeezing, input reconstruction with autoencoders, and ensemble decision fusion. The experimentation employs benchmarking cybersecurity datasets (such as NSL-KDD, CICIDS2017, and Malimg) and models (e.g., SVM, Random Forest, CNN and DNN). Attack algorithms such as FGSM, PGD, DeepFool, and Carlini-Wagner are used. A significant improvement in robustness with accuracy recovery estimated to be between 15–30% improvement with the baseline and hybrid framework. This framework aims to offer a robust framework for constructing secure and resilient software systems that are driven by AI in the cybersecurity applications context.

Introduction

Machine Learning (ML) and Deep Learning (DL) are widely used in cybersecurity tasks such as intrusion detection, malware classification, phishing detection, and spam filtering because they can learn complex patterns and detect threats faster than rule-based systems. However, these models are highly vulnerable to adversarial attacks, where small, imperceptible perturbations cause misclassification while appearing normal to humans. Attacks such as FGSM, PGD, DeepFool, and Carlini–Wagner can bypass traditional defenses like defensive distillation and gradient masking. Therefore, there is a strong need for robust and trustworthy ML-based cybersecurity systems.

The research proposes a hybrid adversarial defense framework that integrates adversarial training, feature squeezing, autoencoder-based input reconstruction, and ensemble decision fusion. The system is evaluated on three cybersecurity datasets—NSL-KDD, CICIDS2017, and Malimg—and tested against four attack algorithms. The goal is to build a highly secure ML model suitable for real-world threat detection.

The proposed system architecture consists of data preprocessing, baseline ML/DL training, adversarial attack generation, a hybrid defense module, and performance evaluation. Feature squeezing reduces bit depth and removes high-frequency adversarial noise; autoencoders reconstruct cleaner inputs; adversarial training improves generalization; and ensemble learning reduces reliance on any single classifier. The system also supports continuous learning by updating models with newly detected adversarial patterns.

Experiments show that adversarial attacks severely reduce accuracy—for example, CNN performance drops by 34% under PGD and CW attacks. With the hybrid defense applied, robustness improves significantly: CNN recovers about 28% accuracy, while SVM and Random Forest models improve from 65.8% to 84.1% and 68.2% to 86.5%, respectively. These results confirm that the defense is effective across different model types.

Overall, the hybrid defense approach offers substantial improvements in adversarial robustness (25–30% on average) without large computational overhead. The layered defense strategy—combining adversarial training, feature smoothing, reconstruction, and ensemble voting—proves far more reliable than any single technique. The study demonstrates that strong, continuously adaptive defense mechanisms are essential for secure ML deployment in modern cybersecurity environments.

Conclusion

In this work, a hybrid adversarial defense framework was proposed to enhance the robustness of machine learning models used in cybersecurity applications. This work showed that traditional ML and DL models like SVM, Random Forest, CNN, and DNN have high accuracy on clean datasets but suffer significant performance degradation when exposed to adversarial attacks including FGSM, PGD, DeepFool, and CW. Against this vulnerability, a multi-layered defense strategy incorporating adversarial training, feature squeezing, autoencoder-based reconstruction, and ensemble fusion was introduced. Experimental evaluation with benchmark cybersecurity datasets like NSL-KDD, CICIDS2017, and Malimg has established the efficacy of the proposed system. The hybrid defense significantly enhances the classification accuracy under adversarial conditions and reduces false positives and false negatives among the evaluated models. From the models evaluated, deep learning architectures like CNN and DNN exhibit the highest robustness gain after employing the defense mechanisms. The ensemble further stabilizes the model predictions by reducing the chances of misclassification specific to an attack. These results confirm that a combination of lightweight defense techniques provides a practical and scalable solution toward enhancing the security of ML-based cybersecurity systems. The proposed framework proves to be more resilient against a large spectrum of adversarial attacks and can support real-time deployments within dynamic cyber environments.

References

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Copyright

Copyright © 2025 Karshana B G, Dr. Vairam T. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.