AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A stacked ensemble ML model combining Random Forest and Logistic Regression and Logistic Regression as a meta-classifier, while Extreme Gradient Boosting as a meta-classifier is proposed in this study to improve software system cyberattack detection.
Cloud computing has emerged as an important core to the contemporary digital services, facilitating scalable, on demand provisioning of resources across a variety of application fields. Nevertheless, this multi-tenant and dynamic environment of clouds and the amplified attack surface make the detection of intrusions through reliable methods a consistent issue that cloud security systems struggle with. The proposed work is a Generative Adversarial Network (GAN)-based hardening framework of cloud intrusion detection systems, targeting better resilience to changing and low-rate cyberattacks. The methodology combines a conditional generator which is used to generate realistic cloud-specific attack traffic, a discriminator used to refine the adversarial traffic, as well as a co-trained intrusion classifier trained on both clean and synthetic data in a closed-loop way. The feature-aware regularization is introduced to maintain the statistical consistency of network traffic, and optimize the attack diversity. The proposed approach is proved to yield better results in comparison with signature-based, machine learning, deep learning, and adversarial ML-based IDS models by experimental assessment. Significant gains in the accuracy of identifying, the ability to recall, stability, and minimizing errors are also noticed with quantifiable increases observed in all evaluation measures. These findings represent the usefulness of adversarial data-driven learning to develop robust, adaptive, and future-ready cloud intrusion detection systems.
A multi-layered intelligent detection system that unites supervised learning, unsupervised anomaly analysis, and ensemble decision strategies to identify network intrusions, malicious software activity, and stealthy advanced persistent threats in near real time is introduced.
A hybrid SDN-based IDS framework that integrates Generative Adversarial Networks (GANs) to handle imbalanced datasets, one-way ANOVA and Genetic Algorithm for feature selection, baseline classifier optimization using Grid Search and Explainable AI techniques to achieve robust, accurate, and interpretable intrusion detection.
A detailed overview of the security risks associated with adversarial attacks is offered, including evasion attacks carried out at inference time, data poisoning that corrupts the training process, backdoor insertion that hides dormant triggers inside a model, and model inversion that leaks private information back out of a trained system.
The review explores the key adversarial attack classes: poisoning, evasion, model extraction, model extraction, model inversion, and membership inference and also white-box, black-box, and grey-box threat models.
An intelligent DDoS detection and mitigation framework that combines classical Machine Learning (ML) classifiers with Deep Learning (DL) architectures to achieve high-fidelity, low-latency attack identification across heterogeneous network topologies is presented.
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