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

Showing 621630 of 800 papers

TitleStatusHype
SparseIDS: Learning Packet Sampling with Reinforcement LearningCode1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners0
IoT Behavioral Monitoring via Network Traffic Analysis0
Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process0
RePAD: Real-time Proactive Anomaly Detection for Time Series0
Pelican: A Deep Residual Network for Network Intrusion Detection0
Cyber Attack Detection thanks to Machine Learning AlgorithmsCode1
A Content-Based Deep Intrusion Detection System0
Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure0
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Benchmark Results

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