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 401–410 of 800 papers

TitleStatusHype
Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes—0
Many Field Packet Classification with Decomposition and Reinforcement Learning—0
On Generalisability of Machine Learning-based Network Intrusion Detection Systems—0
Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection—0
Anomaly Detection in Intra-Vehicle Networks—0
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks—0
An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks—0
A review of Federated Learning in Intrusion Detection Systems for IoT—0
Euler: Detecting Network Lateral Movement via Scalable Temporal Link PredictionCode1
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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