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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–210 of 800 papers

TitleStatusHype
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
An Identification System Using Eye Detection Based On Wavelets And Neural Networks—0
Change Detection in Noisy Dynamic Networks: A Spectral Embedding Approach—0
Characterization of Neural Networks Automatically Mapped on Automotive-grade Microcontrollers—0
Clustering Algorithm to Detect Adversaries in Federated Learning—0
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices—0
CND-IDS: Continual Novelty Detection for Intrusion Detection Systems—0
CoAP-DoS: An IoT Network Intrusion Dataset—0
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques—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