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 261–270 of 800 papers

TitleStatusHype
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System—0
Adversarial Evasion Attacks Practicality in Networks: Testing the Impact of Dynamic Learning—0
A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks—0
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods—0
BEBP: An Poisoning Method Against Machine Learning Based IDSs—0
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection—0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems—0
Bayesian Hyperparameter Optimization for Deep Neural Network-Based Network Intrusion Detection—0
BayBFed: Bayesian Backdoor Defense for Federated 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