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 411–420 of 800 papers

TitleStatusHype
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT—0
LBDMIDS: LSTM Based Deep Learning Model for Intrusion Detection Systems for IoT Networks—0
Learning automata based SVM for intrusion detection—0
Learning-Based Detection of Malicious Volt-VAr Control Parameters in Smart Inverters—0
Learning detectors of malicious web requests for intrusion detection in network traffic—0
Learning in Multiple Spaces: Few-Shot Network Attack Detection with Metric-Fused Prototypical Networks—0
Learning Privately from Multiparty Data—0
Learning to Detect: A Data-driven Approach for Network Intrusion Detection—0
Learning With Differential Privacy—0
LEMDA: A Novel Feature Engineering Method for Intrusion Detection in IoT 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