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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 576–600 of 800 papers

TitleStatusHype
Sorting out typicality with the inverse moment matrix SOS polynomial—0
Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection—0
StatAvg: Mitigating Data Heterogeneity in Federated Learning for Intrusion Detection Systems—0
Statistical Detection of Adversarial examples in Blockchain-based Federated Forest In-vehicle Network Intrusion Detection Systems—0
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles—0
Strategic Deployment of Honeypots in Blockchain-based IoT Systems—0
Strengthening Network Intrusion Detection in IoT Environments with Self-Supervised Learning and Few Shot Learning—0
Towards Explainable Meta-Learning for DDoS Detection—0
Supervised Contrastive ResNet and Transfer Learning for the In-vehicle Intrusion Detection System—0
Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review—0
Survey of Graph Neural Network for Internet of Things and NextG Networks—0
Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things—0
Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process—0
Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applications—0
SynGAN: Towards Generating Synthetic Network Attacks using GANs—0
Synthetic flow-based cryptomining attack generation through Generative Adversarial Networks—0
Systematic Evaluation of Synthetic Data Augmentation for Multi-class NetFlow Traffic—0
Systematic Review: Anomaly Detection in Connected and Autonomous Vehicles—0
TAD: Transfer Learning-based Multi-Adversarial Detection of Evasion Attacks against Network Intrusion Detection Systems—0
TANTRA: Timing-Based Adversarial Network Traffic Reshaping Attack—0
TEAM: Temporal Adversarial Examples Attack Model against Network Intrusion Detection System Applied to RNN—0
Technical Report: Generating the WEB-IDS23 Dataset—0
Temporal Analysis of NetFlow Datasets for Network Intrusion Detection Systems—0
TEST: an End-to-End Network Traffic Examination and Identification Framework Based on Spatio-Temporal Features Extraction—0
The Dendritic Cell Algorithm for Intrusion Detection—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