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 361370 of 800 papers

TitleStatusHype
Extending Signature-based Intrusion Detection Systems WithBayesian Abductive Reasoning0
Extreme bandits0
Fair Anomaly Detection For Imbalanced Groups0
Fast Feature Reduction in intrusion detection datasets0
FastPacket: Towards Pre-trained Packets Embedding based on FastText for next-generation NIDS0
Feasibility of Non-Line-of-Sight Integrated Sensing and Communication at mmWave0
Feature Analysis for Machine Learning-based IoT Intrusion Detection0
Feature Distribution Shift Mitigation with Contrastive Pretraining for Intrusion Detection0
Feature Extraction for Machine Learning-based Intrusion Detection in IoT Networks0
Feature Selection for Network Intrusion Detection0
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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