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 751–760 of 800 papers

TitleStatusHype
Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved FeaturesCode0
Novel Sensor Scheduling Scheme for Intruder Tracking in Energy Efficient Sensor Networks—0
Accelerating Dependency Graph Learning from Heterogeneous Categorical Event Streams via Knowledge Transfer—0
Energy-based Models for Video Anomaly Detection—0
Model Selection for Anomaly Detection—0
A Machine Learning Based Intrusion Detection System for Software Defined 5G Network—0
Hybrid Isolation Forest - Application to Intrusion DetectionCode0
Data Mining model in the discovery of trends and patterns of intruder attacks on the data network as a public-sector innovation—0
Threat analysis of IoT networks Using Artificial Neural Network Intrusion Detection System—0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network—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