SOTAVerified

Intrusion Detection

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 401–425 of 800 papers

TitleStatusHype
A Machine Learning Based Intrusion Detection System for Software Defined 5G Network—0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics—0
A model for multi-attack classification to improve intrusion detection performance using deep learning approaches—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
A multiagent based framework secured with layered SVM-based IDS for remote healthcare systems—0
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks—0
An Adversarial Approach for Explainable AI in Intrusion Detection Systems—0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System—0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks—0
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance—0
An AutoML-based approach for Network Intrusion Detection—0
An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners—0
Sorting out typicality with the inverse moment matrix SOS polynomial—0
Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection—0
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems—0
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems—0
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles—0
Strategic Deployment of Honeypots in Blockchain-based IoT Systems—0
Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning—0
Towards Explainable Meta-Learning for DDoS Detection—0
Supervised Contrastive ResNet and Transfer Learning for the In-vehicle Intrusion Detection System—0
Show:102550
← PrevPage 17 of 32Next →
All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified