Cse Cic Ids2018

CSE-CIC-IDS2018 is a network intrusion detection dataset used extensively to evaluate machine learning models for cybersecurity. Current research focuses on improving the accuracy and efficiency of intrusion detection systems using various deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and graph neural networks (GNNs), often addressing challenges like class imbalance and cross-dataset generalization. These efforts aim to enhance the ability of intrusion detection systems to accurately identify and classify diverse cyberattacks in real-world scenarios, improving network security. The dataset's continued use highlights the ongoing need for robust and adaptable intrusion detection methods.

Papers

October 26, 2024

Advanced Cyberattack Detection in Internet of Medical Things (IoMT) Using Convolutional Neural Networks
Alireza Mohammadi, Hosna Ghahramani, Seyyed Amir Asghari, Mehdi Aminian
Convolutional Neural Network Internet of Medical Thing Security Challenge Cyber Attack Detection Cse Cic Ids2018

July 8, 2024

Multi-agent Reinforcement Learning-based Network Intrusion Detection System
Amine Tellache, Amdjed Mokhtari, Abdelaziz Amara Korba, Yacine Ghamri-Doudane
Intrusion Detection Network Intrusion Detection Intrusion Detection System Cse Cic Ids2018

May 28, 2024

Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems
Afrah Gueriani, Hamza Kheddar, Ahmed Cherif Mazari
Deep Learning Convolutional Neural Network Long Short Term Memory Internet of Thing Intrusion Detection System Cse Cic Ids2018

March 3, 2024

Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network
Renjie Xu, Guangwei Wu, Weiping Wang, Xing Gao, An He, Zhengpeng Zhang
Graph Neural Network Self Supervised Learning Self Supervised Network Intrusion Detection Network Flow Cse Cic Ids2018

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 30, 2024

Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case Study
Adrian Pekar, Richard Jozsa
Anomaly Detection Data Set Case Study Network Traffic Based Anomaly Detection Cse Cic Ids2018 Network Security Network Traffic Anomaly Detection

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

July 21, 2023

Identifying Relevant Features of CSE-CIC-IDS2018 Dataset for the Development of an Intrusion Detection System
László Göcs, Zsolt Csaba Johanyák
Feature Selection Development Activity Intrusion Detection System Feature Relevance ID Problem Cse Cic Ids2018

May 7, 2023

Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Network
Xiaoyu Yang, Lijian Xu, Simon Yu, Qing Xia, Hongsheng Li, Shaoting Zhang
Segmentation Based Approach Coronary Artery Cse Cic Ids2018

November 8, 2022

A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System
Zong-Zhi Lin, Thomas D. Pike, Mark M. Bailey, Nathaniel D. Bastian
Network Intrusion Detection Ensemble Machine Learning Cse Cic Ids2018

August 20, 2022

Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset
Yuhua Yin, Julian Jang-Jaccard, Fariza Sabrina, Jin Kwak
Intrusion Detection System Hierarchical Clustering Unsupervised Clustering Multi Class Anomaly Detection Cse Cic Ids2018

January 24, 2022

Describe me if you can! Characterized Instance-level Human Parsing
Angelique Loesch, Romaric Audigier
Human Parsing Visual Description Cse Cic Ids2018 Instance Level Human Parsing

Cse Cic Ids2018

Papers

Advanced Cyberattack Detection in Internet of Medical Things (IoMT) Using Convolutional Neural Networks

Multi-agent Reinforcement Learning-based Network Intrusion Detection System

Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems

Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network

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

Evaluating ML-Based Anomaly Detection Across Datasets of Varied Integrity: A Case Study

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

Identifying Relevant Features of CSE-CIC-IDS2018 Dataset for the Development of an Intrusion Detection System

Segmentation and Vascular Vectorization for Coronary Artery by Geometry-based Cascaded Neural Network

A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System

Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset

Describe me if you can! Characterized Instance-level Human Parsing