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 661670 of 800 papers

TitleStatusHype
A Combination of Temporal Sequence Learning and Data Description for Anomaly-based NIDS0
A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection0
A comparative evaluation of novelty detection algorithms for discrete sequences0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures0
A Comparative Study on Unsupervised Anomaly Detection for Time Series: Experiments and Analysis0
A Compendium on Network and Host based Intrusion Detection Systems0
A concise method for feature selection via normalized frequencies0
A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks0
A Content-Based Deep Intrusion Detection System0
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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
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1MSTREAM-AEAUC0.9Unverified