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 721–730 of 800 papers

TitleStatusHype
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
An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron—0
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data—0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods—0
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System—0
Show:102550
← PrevPage 73 of 80Next →
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