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 181190 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
A Generative Model Based Honeypot for Industrial OPC UA CommunicationCode0
Implementing Lightweight Intrusion Detection System on Resource Constrained DevicesCode0
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