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 531–540 of 800 papers

TitleStatusHype
Network Activities Recognition and Analysis Based on Supervised Machine Learning Classification Methods Using J48 and Naïve Bayes Algorithm—0
Performance Analysis of a Foreground Segmentation Neural Network Model—0
Intrusion Detection System in Smart Home Network Using Bidirectional LSTM and Convolutional Neural Networks Hybrid Model—0
Cybersecurity Anomaly Detection in Adversarial Environments—0
ADASYN-Random Forest Based Intrusion Detection Model—0
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means—0
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT—0
Robustness of ML-Enhanced IDS to Stealthy Adversaries—0
Adversarial Training for Deep Learning-based Intrusion Detection Systems—0
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets—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