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 451–460 of 800 papers

TitleStatusHype
Moving Object Classification with a Sub-6 GHz Massive MIMO Array using Real Data—0
Multi-agent Reinforcement Learning-based Network Intrusion Detection System—0
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications—0
Multibit Tries Packet Classification with Deep Reinforcement Learning—0
Multi-centrality Graph Spectral Decompositions and their Application to Cyber Intrusion Detection—0
Multiclass Classification Procedure for Detecting Attacks on MQTT-IoT Protocol—0
Multidomain transformer-based deep learning for early detection of network intrusion—0
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems—0
Multi-Source Data Fusion for Cyberattack Detection in Power Systems—0
Multi-stage Attack Detection and Prediction Using Graph Neural Networks: An IoT Feasibility Study—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