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 371380 of 800 papers

TitleStatusHype
CADeSH: Collaborative Anomaly Detection for Smart Homes0
EdgeServe: A Streaming System for Decentralized Model Serving0
Deep Neural Networks based Meta-Learning for Network Intrusion Detection0
Anomaly based network intrusion detection for IoT attacks using deep learning technique0
IoT Botnet Detection Using an Economic Deep Learning Model0
Behavioural Reports of Multi-Stage MalwareCode0
Towards Adversarial Realism and Robust Learning for IoT Intrusion Detection and Classification0
Leveraging Planning Landmarks for Hybrid Online Goal Recognition0
Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach0
BayBFed: Bayesian Backdoor Defense for Federated Learning0
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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