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 221230 of 800 papers

TitleStatusHype
IT Intrusion Detection Using Statistical Learning and Testbed Measurements0
MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs0
On the Cross-Dataset Generalization of Machine Learning for Network Intrusion DetectionCode1
Utilizing Deep Learning for Enhancing Network Resilience in Finance0
ROSpace: Intrusion Detection Dataset for a ROS2-Based Cyber-Physical SystemCode0
Multiclass Classification Procedure for Detecting Attacks on MQTT-IoT Protocol0
Feature Selection using the concept of Peafowl Mating in IDS0
X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection SystemCode0
Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection0
Past, Present, Future: A Comprehensive Exploration of AI Use Cases in the UMBRELLA IoT Testbed0
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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