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

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Showing 221–230 of 800 papers

TitleStatusHype
Anomaly Detection Dataset for Industrial Control Systems—0
Convolutional Neural Network for Intrusion Detection System In Cyber Physical Systems—0
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond—0
C-RADAR: A Centralized Deep Learning System for Intrusion Detection in Software Defined Networks—0
Creating an Explainable Intrusion Detection System Using Self Organizing Maps—0
CSAGC-IDS: A Dual-Module Deep Learning Network Intrusion Detection Model for Complex and Imbalanced Data—0
Cyber Anomaly Detection Using Graph-node Role-dynamics—0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
Cyber Intrusion Detection by Using Deep Neural Networks with Attack-sharing Loss—0
A Survey for Deep Reinforcement Learning Based Network Intrusion Detection—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

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