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 171–180 of 800 papers

TitleStatusHype
Anomaly Detection in Intra-Vehicle Networks—0
Cybersecurity Anomaly Detection in Adversarial Environments—0
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection—0
A Grassmannian Approach to Zero-Shot Learning for Network Intrusion Detection—0
A Cutting-Edge Deep Learning Method For Enhancing IoT Security—0
CRUPL: A Semi-Supervised Cyber Attack Detection with Consistency Regularization and Uncertainty-aware Pseudo-Labeling in Smart Grid—0
Anomaly Detection Dataset for Industrial Control Systems—0
Anomaly based network intrusion detection for IoT attacks using deep learning technique—0
Active Learning for Wireless IoT Intrusion Detection—0
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
Show:102550
← PrevPage 18 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