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

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Showing 71–80 of 800 papers

TitleStatusHype
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks—0
Detecting APT Malware Command and Control over HTTP(S) Using Contextual Summaries—0
Technical Report: Generating the WEB-IDS23 Dataset—0
Implementing Large Quantum Boltzmann Machines as Generative AI Models for Dataset Balancing—0
Gotham Dataset 2025: A Reproducible Large-Scale IoT Network Dataset for Intrusion Detection and Security ResearchCode0
Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDSCode0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
Investigating Application of Deep Neural Networks in Intrusion Detection System Design—0
A Transfer Learning Framework for Anomaly Detection in Multivariate IoT Traffic Data—0
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All datasetsCICIDS2017CIC-DDoSCIC-DoSUNSW-NB15

Benchmark Results

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