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 291300 of 800 papers

TitleStatusHype
Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction0
3D-IDS: Doubly Disentangled Dynamic Intrusion DetectionCode1
Planning Landmark Based Goal Recognition Revisited: Does Using Initial State Landmarks Make Sense?0
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices0
Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection0
OptIForest: Optimal Isolation Forest for Anomaly DetectionCode0
Online Self-Supervised Deep Learning for Intrusion Detection Systems0
Host-Based Network Intrusion Detection via Feature Flattening and Two-stage Collaborative Classifier0
Is there a Trojan! : Literature survey and critical evaluation of the latest ML based modern intrusion detection systems in IoT environments0
Intrusion Detection: A Deep Learning Approach0
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Benchmark Results

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