Network and cybersecurity applications of defense in adversarial attacks: A state-of-the-art using machine learning and deep learning methods

YL Khaleel, MA Habeeb, AS Albahri… - Journal of Intelligent …, 2024 - degruyter.com
This study aims to perform a thorough systematic review investigating and synthesizing
existing research on defense strategies and methodologies in adversarial attacks using …

Unified robust network embedding framework for community detection via extreme adversarial attacks

W Zhu, C Chen, B Peng - Information Sciences, 2023 - Elsevier
Graph data are widely available in complex network systems. Numerous community
detection algorithms have been investigated to study graph problems wherein the network …

A two-stage co-adversarial perturbation to mitigate out-of-distribution generalization of large-scale graph

Y Wang, H Xue, X Wang - Expert Systems with Applications, 2024 - Elsevier
In the realm of graph out-of-distribution (OOD), despite recent strides in advancing graph
neural networks (GNNs) for the modeling of graph data, training GNNs on large-scale …

Interpretable adversarial neural pairwise ranking for academic network embedding

A Paul, Z Wu, B Chen, K Luo, L Fang - Knowledge and Information …, 2025 - Springer
Bayesian personalized ranking (BPR) has gained prominence as an effective method for
pairwise learning, particularly in personalized tasks such as recommendation systems …