Network Intrusion Detection System

Network Intrusion Detection Systems (NIDS) aim to identify malicious activity within network traffic, a critical task in cybersecurity. Current research heavily focuses on machine learning (ML)-based NIDS, employing various algorithms like decision trees, random forests, and neural networks (including LSTMs and Bayesian Neural Networks) to improve detection accuracy and robustness. However, challenges remain in ensuring generalization across diverse datasets and mitigating adversarial attacks, with ongoing efforts concentrating on feature selection, data augmentation, and uncertainty quantification to enhance model reliability and trustworthiness. These advancements are crucial for improving the effectiveness and security of real-world network infrastructure.

Papers

September 11, 2024

Introducing Perturb-ability Score (PS) to Enhance Robustness Against Evasion Adversarial Attacks on ML-NIDS
Mohamed elShehaby, Ashraf Matrawy
Adversarial Attack Native Robustness Network Intrusion Detection Feature Perturbation Adversarial Evasion Attack Network Intrusion Detection System

May 20, 2024

Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS
Maho Kajiura, Junya Nakamura
Network Intrusion Detection Processing Framework Network Intrusion Detection System Network Attack Based NIDS Real World Performance

February 15, 2024

On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detection
Marco Cantone, Claudio Marrocco, Alessandro Bria
Machine Learning Network Intrusion Detection Cross Dataset Network Intrusion Detection System Cse Cic Ids2018

January 22, 2024

Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction
Md. Alamin Talukder, Md. Manowarul Islam, Md Ashraf Uddin, Khondokar Fida Hasan, Selina Sharmin, Salem A. Alyami, Mohammad Ali Moni
New Machine Network Intrusion Detection Intrusion Detection System Imbalanced Data Content Based Feature Oversampling Method Network Intrusion Network Intrusion Detection System Cse Cic Ids2018

October 30, 2023

Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection
Noah Ziems, Gang Liu, John Flanagan, Meng Jiang
Natural Language Decision Tree Network Intrusion Detection Interpretable Tree Network Intrusion Detection System

September 5, 2023

Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification
Jacopo Talpini, Fabio Sartori, Marco Savi
Uncertainty Quantification Intrusion Detection System Classification Uncertainty Network Intrusion Detection System

June 8, 2023

Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning
Mohamed el Shehaby, Ashraf Matrawy
Adversarial Attack Global Impact Network Programming Dynamic Learning Adversarial Evasion Attack Network Intrusion Detection System Based NIDS

December 26, 2022

Investigation and rectification of NIDS datasets and standardized feature set derivation for network attack detection with graph neural networks
Anton Raskovalov, Nikita Gabdullin, Vasily Dolmatov
Neural Network Graph Drawing Internet of Thing Intrusion Detection Network Intrusion Detection Network Intrusion Detection System

May 9, 2022

On Generalisability of Machine Learning-based Network Intrusion Detection Systems
Siamak Layeghy, Marius Portmann
Machine Learning Unsupervised Learning Stronger Generalizability Network Intrusion Detection Network Intrusion Detection System

March 9, 2022

The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems
Giovanni Apruzzese, Luca Pajola, Mauro Conti
Intrusion Detection Network Intrusion Detection Supervised Machine Learning Network Intrusion Detection System Cross Article Comparison

February 20, 2022

NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks
Haoyu Liu, Paul Patras
Deep Learning Approach Network Intrusion Detection Attack Success Rate Network Intrusion Detection System

November 4, 2021

A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection
Mohanad Sarhan, Siamak Layeghy, Nour Moustafa, Marius Portmann
Network Intrusion Detection Threat Intelligence Network Intrusion Detection System

Network Intrusion Detection System

Papers

Introducing Perturb-ability Score (PS) to Enhance Robustness Against Evasion Adversarial Attacks on ML-NIDS

Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS

On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detection

Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction

Explaining Tree Model Decisions in Natural Language for Network Intrusion Detection

Enhancing Trustworthiness in ML-Based Network Intrusion Detection with Uncertainty Quantification

Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning

Investigation and rectification of NIDS datasets and standardized feature set derivation for network attack detection with graph neural networks

On Generalisability of Machine Learning-based Network Intrusion Detection Systems

The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems

NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks

A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection