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Found 13 papers, 2 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.
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).
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection
The framework has been designed and evaluated in this paper by using two key datasets in a NetFlow format known as NF-UNSW-NB15-v2 and NF-BoT-IoT-v2.
From Zero-Shot Machine Learning to Zero-Day Attack Detection
The standard ML methodology assumes that the test samples are derived from a set of pre-observed classes used in the training phase.
Feature Analysis for Machine Learning-based IoT Intrusion Detection
This demonstrates a significant potential to reduce the computational and storage cost of intrusion detection systems while maintaining near-optimal detection accuracy.
Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks
In an analysis of related works, it was observed that most researchers aim to obtain better classification results by using a set of untried combinations of Feature Reduction (FR) and Machine Learning (ML) techniques on NIDS datasets.
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection
Therefore, two feature sets (NetFlow and CICFlowMeter) have been evaluated in terms of detection accuracy across three key datasets, i. e., CSE-CIC-IDS2018, BoT-IoT, and ToN-IoT.
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).
Exploring Edge TPU for Network Intrusion Detection in IoT
The focus of this paper is the exploration of deep learning-based NIDS at the edge of IoT, and in particular the computational and energy efficiency.
