Reproducing a Machine Learning-Driven Access Control and Intrusion Detection Framework for Hybrid Cloud Environments From Anonymity-Preserving Encryption to Adversarially Robust Detection

Hybrid cloud environments combine the elasticity of public infrastructure with the control of private deployments, but they also widen the surface through which data confidentiality, user privacy, and system availability can be attacked. This paper reports an independent reproduction of a four-part machine learning-driven security framework for hybrid clouds originally proposed by Mulimani et al. The reproduced components are an anonymity-preserving, collusion- resistant symmetric encryption scheme; a two-level supervised classification framework for role-based access control, together with an attribute-based access control architecture built on recursive feature elimination; a dynamic intrusion detection system combining Linear Discriminant Analysis feature reduction, an AdaBoost ensemble, and class-by-class K-means clustering; and an adversarial robustness analysis using FGSM and DeepFool attacks with same-family and cross-adversarial training. On the NSL-KDD dataset, the reproduction obtains 98.10 percent accuracy for Naive Bayes binary classification, 99.26 percent for multinomial logistic regression attack-category classification, 93.16 percent for the AdaBoost ensemble, and per- class F1 scores of 0.76 to 0.88 for hierarchical clustering. Undefended FGSM and DeepFool attacks reduce classifier accuracy from 91 to 99 percent to as low as 16 percent. Same- family adversarial training largely restores accuracy, whereas cross-adversarial training reveals strongly direction-dependent robustness, with the Support Vector Machine generalizing most consistently. The paper also documents the limitations of the reproduction, including reliance on a single dated dataset, a synthetic role attribute, and single-split evaluation.