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 211220 of 800 papers

TitleStatusHype
Building an Effective Intrusion Detection System using Unsupervised Feature Selection in Multi-objective Optimization Framework0
Collective Anomaly Detection based on Long Short Term Memory Recurrent Neural Network0
BS-GAT Behavior Similarity Based Graph Attention Network for Network Intrusion Detection0
Conformalized density- and distance-based anomaly detection in time-series data0
A New Intrusion Detection System using the Improved Dendritic Cell Algorithm0
Constrained Twin Variational Auto-Encoder for Intrusion Detection in IoT Systems0
Breaking the Flow and the Bank: Stealthy Cyberattacks on Water Network Hydraulics0
Adversarial Machine Learning in Network Intrusion Detection Systems0
A Content-Based Deep Intrusion Detection System0
A cognitive based Intrusion detection system0
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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