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 481–490 of 800 papers

TitleStatusHype
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack—0
Omni SCADA Intrusion Detection Using Deep Learning Algorithms—0
One-Class Classification for Intrusion Detection on Vehicular Networks—0
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks—0
One-Shot Learning on Attributed Sequences—0
On Generalisability of Machine Learning-based Network Intrusion Detection Systems—0
Onion-Peeling Outlier Detection in 2-D data Sets—0
Online Dictionary Learning Based Fault and Cyber Attack Detection for Power Systems—0
Online Feature Ranking for Intrusion Detection Systems—0
Online Self-Supervised Deep Learning for Intrusion Detection Systems—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