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 571–580 of 800 papers

TitleStatusHype
Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On AutoencoderCode0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS—0
Fragments Expert A Graphical User Interface MATLAB Toolbox for Classification of File FragmentsCode0
RNNIDS: Enhancing Network Intrusion Detection Systems through Deep Learning—0
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape—0
Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach—0
Intrusion detection in computer systems by using artificial neural networks with Deep Learning approaches—0
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems—0
Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning—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