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 771780 of 800 papers

TitleStatusHype
Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the EdgeCode0
Intrusion Detection In Computer Networks Using Machine Learning AlgorithmsCode0
Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDSCode0
Sparse Bayesian approach for metric learning in latent spaceCode0
Are Existing Out-Of-Distribution Techniques Suitable for Network Intrusion Detection?Code0
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion DetectionCode0
CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks SimulationCode0
A Generative Model Based Honeypot for Industrial OPC UA CommunicationCode0
Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification SystemsCode0
Benchmarking Unsupervised Online IDS for Masquerade Attacks in CANCode0
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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