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 1120 of 800 papers

TitleStatusHype
A new Deep Learning Based Intrusion Detection System for Cloud SecurityCode1
An Intrusion Detection System based on Deep Belief NetworksCode1
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly DetectionCode1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated LearningCode1
ARGUS: Context-Based Detection of Stealthy IoT Infiltration AttacksCode1
Bridging the gap to real-world for network intrusion detection systems with data-centric approachCode1
A Study on Transferability of Deep Learning Models for Network Intrusion DetectionCode1
A flow-based IDS using Machine Learning in eBPFCode1
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
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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