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 501510 of 800 papers

TitleStatusHype
PacketCLIP: Multi-Modal Embedding of Network Traffic and Language for Cybersecurity Reasoning0
Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed0
Payload-Aware Intrusion Detection with CMAE and Large Language Models0
PCAP-Backdoor: Backdoor Poisoning Generator for Network Traffic in CPS/IoT Environments0
Pelican: A Deep Residual Network for Network Intrusion Detection0
Performance Analysis of a Foreground Segmentation Neural Network Model0
Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection0
Performance Comparison of Intrusion Detection Systems and Application of Machine Learning to Snort System0
Performance Evaluation of Machine Learning Techniques for DoS Detection in Wireless Sensor Network0
PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection0
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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