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 721730 of 800 papers

TitleStatusHype
AI-based Two-Stage Intrusion Detection for Software Defined IoT Networks0
Intensive Preprocessing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques0
EMO\&LY (EMOtion and AnomaLY) : A new corpus for anomaly detection in an audiovisual stream with emotional context.0
Towards an Efficient Anomaly-Based Intrusion Detection for Software-Defined Networks0
Deep Predictive Coding Neural Network for RF Anomaly Detection in Wireless Networks0
Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection0
Onion-Peeling Outlier Detection in 2-D data Sets0
BEBP: An Poisoning Method Against Machine Learning Based IDSs0
Online Feature Ranking for Intrusion Detection Systems0
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion DetectionCode0
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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