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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 401410 of 800 papers

TitleStatusHype
Jasmine: A New Active Learning Approach to Combat Cybercrime0
Joint Semantic Transfer Network for IoT Intrusion Detection0
Kernel density estimation based sampling for imbalanced class distribution0
Keystroke Patterns as Prosody in Digital Writings: A Case Study with Deceptive Reviews and Essays0
KiNETGAN: Enabling Distributed Network Intrusion Detection through Knowledge-Infused Synthetic Data Generation0
KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data0
KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs0
Large Language Models for Cyber Security: A Systematic Literature Review0
Large Language Models in Wireless Application Design: In-Context Learning-enhanced Automatic Network Intrusion Detection0
Late Breaking Results: Scalable and Efficient Hyperdimensional Computing 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
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1MSTREAM-AEAUC0.9Unverified