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Network Intrusion Detection

Network intrusion detection is the task of monitoring network traffic to and from all devices on a network in order to detect computer attacks.

Papers

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Showing 201–250 of 261 papers

TitleStatusHype
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion DetectionCode0
Network Intrusion Detection Using Wrapper-based Decision Tree for Feature Selection—0
Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection SystemsCode1
Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection—0
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion DetectionCode0
Efficient Deep CNN-BiLSTM Model for Network Intrusion DetectionCode1
Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion DetectorsCode1
Adversarial Machine Learning in Network Intrusion Detection Systems—0
A New Intrusion Detection System using the Improved Dendritic Cell Algorithm—0
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput ApplicationsCode1
Hybrid Model For Intrusion Detection Systems—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process—0
Pelican: A Deep Residual Network for Network Intrusion Detection—0
Synthesis of a Machine Learning Model for Detecting Computer Attacks Based on the CICIDS2017 DatasetCode0
A Robust Comparison of the KDDCup99 and NSL-KDD IoT Network Intrusion Detection Datasets Through Various Machine Learning Algorithms—0
Network Intrusion Detection based on LSTM and Feature Embedding—0
Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response—0
Deep Anomaly Detection with Deviation NetworksCode0
Intrusion Detection using Sequential Hybrid Model—0
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors—0
WOTBoost: Weighted Oversampling Technique in Boosting for imbalanced learning—0
LuNet: A Deep Neural Network for Network Intrusion DetectionCode0
A Transfer Learning Approach for Network Intrusion Detection—0
SynGAN: Towards Generating Synthetic Network Attacks using GANs—0
Sparse Bayesian approach for metric learning in latent spaceCode0
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings—0
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS—0
Mimic Learning to Generate a Shareable Network Intrusion Detection Model—0
Active Learning for Network Intrusion Detection—0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
Rallying Adversarial Techniques against Deep Learning for Network Security—0
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework—0
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks—0
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternativesCode0
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber SecurityCode0
Anomaly Detection via Minimum Likelihood Generative Adversarial Networks—0
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier DetectionCode0
V-CNN: When Convolutional Neural Network encounters Data Visualization—0
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection SystemsCode0
Onion-Peeling Outlier Detection in 2-D data Sets—0
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion DetectionCode0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
A Deep Belief Network Based Machine Learning System for Risky Host Detection—0
A Grassmannian Approach to Zero-Shot Learning for Network Intrusion Detection—0
Security Evaluation of Pattern Classifiers under Attack—0
Energy-based Models for Video Anomaly Detection—0
Model Selection for Anomaly Detection—0
Hybrid Isolation Forest - Application to Intrusion DetectionCode0
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