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 441450 of 800 papers

TitleStatusHype
Man-in-the-Middle Intrusion Detection Based on CNN-LSTM Model0
Many Field Packet Classification with Decomposition and Reinforcement Learning0
Mapping the Landscape of Generative AI in Network Monitoring and Management0
Mimic Learning to Generate a Shareable Network Intrusion Detection Model0
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network0
MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs0
Model Selection for Anomaly Detection0
Modern Cybersecurity Solution using Supervised Machine Learning0
Modern Problems Require Modern Solutions: Hybrid Concepts for Industrial Intrusion Detection0
More Efficient Topic Modelling Through a Noun Only Approach0
Show:102550
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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