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 731–740 of 800 papers

TitleStatusHype
First-order bifurcation detection for dynamic complex networks—0
Generative Models for Spear Phishing Posts on Social Media—0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks—0
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms—0
Secure Mobile Crowdsensing with Deep Learning—0
Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput ComputingCode0
Fusion of ANN and SVM Classifiers for Network Attack Detection—0
Evaluation of Machine Learning Algorithms for Intrusion Detection System—0
Learning automata based SVM for intrusion detection—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