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 111120 of 800 papers

TitleStatusHype
Take Package as Language: Anomaly Detection Using TransformerCode0
Intelligent Green Efficiency for Intrusion Detection0
Sdn Intrusion Detection Using Machine Learning Method0
Securing from Unseen: Connected Pattern Kernels (CoPaK) for Zero-Day Intrusion Detection0
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis0
Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection0
SCGNet-Stacked Convolution with Gated Recurrent Unit Network for Cyber Network Intrusion Detection and Intrusion Type ClassificationCode0
Implementing Lightweight Intrusion Detection System on Resource Constrained DevicesCode0
A Generative Model Based Honeypot for Industrial OPC UA CommunicationCode0
NIDS Neural Networks Using Sliding Time Window Data Processing with Trainable Activations and its Generalization Capability0
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Benchmark Results

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