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 561–570 of 800 papers

TitleStatusHype
Security Evaluation of Support Vector Machines in Adversarial Environments—0
Security Orchestration, Automation, and Response Engine for Deployment of Behavioural Honeypots—0
Segmented Federated Learning for Adaptive Intrusion Detection System—0
Sensor Scheduling in Intrusion Detection Games with Uncertain Payoffs—0
Sequential Binary Classification for Intrusion Detection—0
SFE-GACN: A Novel Unknown Attack Detection Method Using Intra Categories Generation in Embedding Space—0
Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey—0
Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms—0
SIGMA : Strengthening IDS with GAN and Metaheuristics Attacks—0
Simultaneous Intrusion Detection and Localization Using ISAC Network—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