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 511520 of 800 papers

TitleStatusHype
Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?0
POET: A Self-learning Framework for PROFINET Industrial Operations Behaviour0
PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruning0
Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis0
PowerRadio: Manipulate Sensor Measurementvia Power GND Radiation0
PPT-GNN: A Practical Pre-Trained Spatio-Temporal Graph Neural Network for Network Security0
Practical Machine Learning for Cloud Intrusion Detection: Challenges and the Way Forward0
Practical Performance of a Distributed Processing Framework for Machine-Learning-based NIDS0
Precise Feature Selection and Case Study of Intrusion Detection in an Industrial Control System (ICS) Environment0
Predicting Network Attacks Using Ontology-Driven Inference0
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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