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

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Showing 131–140 of 800 papers

TitleStatusHype
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors—0
A New Clustering Approach for Anomaly Intrusion Detection—0
Adversarial Examples in Constrained Domains—0
A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks—0
A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm—0
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System—0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning—0
Accelerating IoV Intrusion Detection: Benchmarking GPU-Accelerated vs CPU-Based ML Libraries—0
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods—0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
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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