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<p>Adversarial perturbation represents an acute problem inadequately covered by current defenses that have not sufficiently secured Machine Learning-based Intrusion Detection System (IDS) running in cloud environments against evasion attacks. Namely, adversarial manipulations can be easily carried out with minimal changes in input data, resulting in failed detection, false-positive classification of attacks as benign traffic, and model corruption without raising traditional alerts. Existing defensive techniques further aggravate the problem, since centralized monolithic models demonstrate vulnerability to adversarial evasion, federated implementations open up data poisoning vulnerabilities due to distributed nature of training, and black-box solutions do not allow explaining decisions in terms comprehensible to humans. In order to tackle this trifecta of challenges, we suggest an innovative Adversarial-Resilient Federated Ensemble Learning (AR-FEL) system. Our solution unites three key ideas: (i) an adversarially trained meta-classifier ensemble consisting of four different Machine Learning classifiers – XGBoost, Random Forest, Gradient Boosting, and Multi-Layer Perceptron (MLP), aggregated into a stacked logistic regression, capable of counteracting both decision boundary adversarial attacks and semantics-based manipulation aimed at deceiving monolithic models; (ii) a linguistic-model-driven post-hack analysis module responsible for interpreting numeric anomaly scores and translating them into human-readable, actionable attack scenarios – an approach that allows overcoming limitations of purely numeric explanations like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME); and (iii) using federated learning, which not only ensures confidentiality of training but also serves as a tool for preventing local data poisoning from being propagated to the global model. The effectiveness of our proposed model was tested against adversarial attacks such as Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Carlini &amp; Wagner (adversarial attack), as well as adversarial perturbation aimed at manipulating model’s decision boundary and semantics. In our experiments, the performance metrics of Adversarial-Resilient Federated Ensemble Learning (AR-FEL) were 99.91% accuracy on the Network Security Lab – Knowledge Discovery and Data Mining (NSL-KDD) dataset (robustness gap: −0.03%), 94.31% accuracy on University of New South Wales Network Benchmark (2015) dataset (− 0.02%), and 71.89% on Canadian Institute for Cybersecurity Intrusion Detection System (2017) dataset (− 0.59%). Moreover, the model demonstrated significantly less degradation compared to any of the other baselines in the experiment. Compared to standalone deep learning model, ensemble learning achieved better resistance by 6.1% and superior cross-domain transferability by 7.0%.</p>

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Adversarial resilience in cloud based machine learning: a unified framework for robust threat detection and forecasting

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Ayaan Sehgal,
  • Vedant Vijay Zade,
  • K. Pooja Karan,
  • Monami Som Saha,
  • Bhawana Tyagi

摘要

Adversarial perturbation represents an acute problem inadequately covered by current defenses that have not sufficiently secured Machine Learning-based Intrusion Detection System (IDS) running in cloud environments against evasion attacks. Namely, adversarial manipulations can be easily carried out with minimal changes in input data, resulting in failed detection, false-positive classification of attacks as benign traffic, and model corruption without raising traditional alerts. Existing defensive techniques further aggravate the problem, since centralized monolithic models demonstrate vulnerability to adversarial evasion, federated implementations open up data poisoning vulnerabilities due to distributed nature of training, and black-box solutions do not allow explaining decisions in terms comprehensible to humans. In order to tackle this trifecta of challenges, we suggest an innovative Adversarial-Resilient Federated Ensemble Learning (AR-FEL) system. Our solution unites three key ideas: (i) an adversarially trained meta-classifier ensemble consisting of four different Machine Learning classifiers – XGBoost, Random Forest, Gradient Boosting, and Multi-Layer Perceptron (MLP), aggregated into a stacked logistic regression, capable of counteracting both decision boundary adversarial attacks and semantics-based manipulation aimed at deceiving monolithic models; (ii) a linguistic-model-driven post-hack analysis module responsible for interpreting numeric anomaly scores and translating them into human-readable, actionable attack scenarios – an approach that allows overcoming limitations of purely numeric explanations like SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME); and (iii) using federated learning, which not only ensures confidentiality of training but also serves as a tool for preventing local data poisoning from being propagated to the global model. The effectiveness of our proposed model was tested against adversarial attacks such as Fast Gradient Sign Method (FGSM), Projected Gradient Descent (PGD), and Carlini & Wagner (adversarial attack), as well as adversarial perturbation aimed at manipulating model’s decision boundary and semantics. In our experiments, the performance metrics of Adversarial-Resilient Federated Ensemble Learning (AR-FEL) were 99.91% accuracy on the Network Security Lab – Knowledge Discovery and Data Mining (NSL-KDD) dataset (robustness gap: −0.03%), 94.31% accuracy on University of New South Wales Network Benchmark (2015) dataset (− 0.02%), and 71.89% on Canadian Institute for Cybersecurity Intrusion Detection System (2017) dataset (− 0.59%). Moreover, the model demonstrated significantly less degradation compared to any of the other baselines in the experiment. Compared to standalone deep learning model, ensemble learning achieved better resistance by 6.1% and superior cross-domain transferability by 7.0%.