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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 691–700 of 800 papers

TitleStatusHype
Adversarial Machine Learning in Network Intrusion Detection Systems—0
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—0
Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems—0
Adversarial Training for Deep Learning-based Intrusion Detection Systems—0
A Dynamic Watermarking Algorithm for Finite Markov Decision Problems—0
A Grassmannian Approach to Zero-Shot Learning for Network Intrusion Detection—0
A Heterogeneous Graph Learning Model for Cyber-Attack Detection—0
A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System—0
A Hybrid Deep Learning Anomaly Detection Framework for Intrusion Detection—0
A Hypergraph-Based Machine Learning Ensemble Network Intrusion Detection System—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