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 441–450 of 800 papers

TitleStatusHype
On Generalisability of Machine Learning-based Network Intrusion Detection Systems—0
Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection—0
Anomaly Detection in Intra-Vehicle Networks—0
Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks—0
An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks—0
A review of Federated Learning in Intrusion Detection Systems for IoT—0
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles—0
ARLIF-IDS -- Attention augmented Real-Time Isolation Forest Intrusion Detection System—0
Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach—0
HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT 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