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

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Showing 371–380 of 800 papers

TitleStatusHype
Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification—0
Feature selection for intrusion detection systems—0
Feature Selection for Network Intrusion Detection—0
Feature Selection using the concept of Peafowl Mating in IDS—0
Efficient Network Traffic Feature Sets for IoT Intrusion Detection—0
Federated Intrusion Detection for IoT with Heterogeneous Cohort Privacy—0
Federated Deep Learning for Intrusion Detection in IoT Networks—0
Federated Learning for Intrusion Detection System: Concepts, Challenges and Future Directions—0
Federated Learning for Intrusion Detection in IoT Security: A Hybrid Ensemble Approach—0
Efficient Network Representation for GNN-based Intrusion Detection—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13—Unverified
2K-Nearest NeighborsAccuracy (%)98.07—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9—Unverified