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 261270 of 800 papers

TitleStatusHype
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures0
A short review on Applications of Deep learning for Cyber security0
A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods0
A Scalable Hierarchical Intrusion Detection System for Internet of Vehicles0
Artificial Neural Network for Cybersecurity: A Comprehensive Review0
A model for multi-attack classification to improve intrusion detection performance using deep learning approaches0
A DDoS-Aware IDS Model Based on Danger Theory and Mobile Agents0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics0
A Robust Comparison of the KDDCup99 and NSL-KDD IoT Network Intrusion Detection Datasets Through Various Machine Learning Algorithms0
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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