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 591–600 of 800 papers

TitleStatusHype
NERD: Neural Network for Edict of Risky Data Streams—0
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs—0
Random Partitioning Forest for Point-Wise and Collective Anomaly Detection -- Application to Intrusion DetectionCode1
Leveraging Siamese Networks for One-Shot Intrusion Detection Model—0
Efficient Deep CNN-BiLSTM Model for Network Intrusion DetectionCode1
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
A Novel SDN Dataset for Intrusion Detection in IoT NetworksCode1
Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks—0
Learning With Differential Privacy—0
G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System—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