Federated learning-driven cloud intrusion detection using transfer learning and ablation frigate-fairy hybrid optimization

Prototype Retrieval-Augmented Federated Learning System for Robust Intrusion Detection

Adaptive intrusion detection system for cloud security using deep learning

The findings confirm that the proposed IDSaaS framework provides an efficient, scalable, and adaptive solution for real-time cloud intrusion detection and significantly enhances the reliability and resilience of modern cloud and industrial cybersecurity infrastructures.

Enhanced Intrusion Detection in IoT Networks using Federated Learning

The results show a success in implementing a real time, scalable, privacy-preserving, and adaptive IDS in large-scale IoT deployments through intelligent workload distribution between edge and cloud layers.

Generative Adversarial Networks for Hardening Cloud Intrusion Detection Systems

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.

Interpretable Deep Learning Defences via User Anomaly Detection in Cloud Platforms

This paper introduces an interpretable deep learning defence framework in the context of user anomaly detection for both IaaS and PaaS cloud service layers and SaaS application layer and shows that the detection accuracy and F1-score are much higher than those of the conventional rule-based and black-box deep learning IDSs.

A hybrid deep reinforcement learning framework for proactive cloud network intrusion detection using spatiotemporal feature learning

ShieldDRLNet is a hybrid deep reinforcement learning framework for proactive cloud-network intrusion detection that employs a convolutional neural network and a long short-term memory encoder to obtain a spatiotemporal traffic representation and uses a Double Deep Q-Network agent for adaptive sequential decision-making.