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
FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems—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
Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks—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