Intrusion detection systems (IDS) play a crucial role in maintaining network security. With the
increasing sophistication of cyber attack methods, traditional detection approaches are …

Cyberattacks represent an ever-growing threat that has become a real priority for most
organizations. Attackers use sophisticated attack scenarios to deceive defense systems in …

Deep neural mapping support vector machine (DNMSVM) has achieved good results in
numerous tasks by mapping the input from a low-dimensional space to a high-dimensional …

Machine Learning (ML) algorithms are largely adopted in modern Network Intrusion
Detection Systems (NIDS). The most recent researches propose the use of Graph Neural …

Considering the robustness and accuracy of conventional intrusion detection models are
easily influenced by adversarial attacks, this work proposes an anti-attack intrusion detection …

In recent years, the use of TLS (Transport Layer Security) protocol to protect communication
information has become increasingly popular as users are more aware of network security …

Link prediction is a crucial problem in the analysis of graph-structured data, and graph
neural networks (GNNs) have proven to be effective in addressing this problem. However …

In recent years, the network security situation has become critical. A key component of
network security is intrusion detection. With the continuous development of deep learning …

The most recent proposals of Machine and Deep Learning algorithms for Network Intrusion
Detection Systems (NIDS) leverage Graph Neural Networks (GNN). These techniques …

In this paper, a blockchain-assisted cross-silo graph federated learning (B-CGFL) framework
is presented for large-scale network intrusion detection, aiming to break down barriers …