Search Results for author:
Found 8 papers, 3 papers with code
FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systems
This paper presents the FlowTransformer framework, a novel approach for implementing transformer-based Network Intrusion Detection Systems (NIDSs).
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection
Therefore, this paper proposes a Deep One-Class (DOC) classifier for network intrusion detection by only training on benign network data samples.
XG-BoT: An Explainable Deep Graph Neural Network for Botnet Detection and Forensics
The proposed model comprises a botnet detector and an explainer for automatic forensics.
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks
This paper investigates Graph Neural Networks (GNNs) application for self-supervised network intrusion and anomaly detection.
HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection
The continuous strengthening of the security posture of IoT ecosystems is vital due to the increasing number of interconnected devices and the volume of sensitive data shared.
Inspection-L: Self-Supervised GNN Node Embeddings for Money Laundering Detection in Bitcoin
The proposed method was evaluated on the Elliptic dataset and shows that our approach outperforms the state-of-the-art in terms of key classification metrics, which demonstrates the potential of self-supervised GNN in the detection of illicit cryptocurrency transactions.
Graph Neural Network-based Android Malware Classification with Jumping Knowledge
This paper presents a new Android malware detection method based on Graph Neural Networks (GNNs) with Jumping-Knowledge (JK).
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
This paper presents a new Network Intrusion Detection System (NIDS) based on Graph Neural Networks (GNNs).
