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 651–660 of 800 papers

TitleStatusHype
Change Detection in Noisy Dynamic Networks: A Spectral Embedding Approach—0
Intrusion detection systems using classical machine learning techniques versus integrated unsupervised feature learning and deep neural network—0
K-Metamodes: frequency- and ensemble-based distributed k-modes clustering for security analyticsCode0
LuNet: A Deep Neural Network for Network Intrusion DetectionCode0
Detecting malicious logins as graph anomalies—0
Walling up Backdoors in Intrusion Detection SystemsCode0
Destination-aware Adaptive Traffic Flow Rule Aggregation in Software-Defined Networks—0
A Transfer Learning Approach for Network Intrusion Detection—0
TEST: an End-to-End Network Traffic Examination and Identification Framework Based on Spatio-Temporal Features Extraction—0
SynGAN: Towards Generating Synthetic Network Attacks using GANs—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