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 791800 of 800 papers

TitleStatusHype
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion DetectionCode0
Diffusion-based Adversarial Purification for Intrusion DetectionCode0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection SystemsCode0
Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection SystemsCode0
X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection SystemCode0
Deep Learning Applications for Intrusion Detection in Network TrafficCode0
Reliable Malware Analysis and Detection using Topology Data AnalysisCode0
Evaluating the Performance of Machine Learning-Based Classification Models for IoT Intrusion DetectionCode0
Separating Flows in Encrypted Tunnel TrafficCode0
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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