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

Showing 6170 of 800 papers

TitleStatusHype
SoK: Pragmatic Assessment of Machine Learning for Network Intrusion DetectionCode1
SparseIDS: Learning Packet Sampling with Reinforcement LearningCode1
Open-Source Framework for Encrypted Internet and Malicious Traffic ClassificationCode1
Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural NetworksCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic ClassificationCode1
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
A Novel SDN Dataset for Intrusion Detection in IoT NetworksCode1
Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Random ForestAccuracy (%)98.13Unverified
2K-Nearest NeighborsAccuracy (%)98.07Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-PCAAUC0.94Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-IBAUC0.95Unverified
#ModelMetricClaimedVerifiedStatus
1MSTREAM-AEAUC0.9Unverified