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 551–600 of 800 papers

TitleStatusHype
RTIDS: A Robust Transformer-Based Approach for Intrusion Detection System—0
RX-ADS: Interpretable Anomaly Detection using Adversarial ML for Electric Vehicle CAN data—0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
Sdn Intrusion Detection Using Machine Learning Method—0
SecureBERT and LLAMA 2 Empowered Control Area Network Intrusion Detection and Classification—0
Secure Mobile Crowdsensing with Deep Learning—0
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning—0
Securing from Unseen: Connected Pattern Kernels (CoPaK) for Zero-Day Intrusion Detection—0
Securing of Unmanned Aerial Systems (UAS) against security threats using human immune system—0
Security Evaluation of Pattern Classifiers under Attack—0
Security Evaluation of Support Vector Machines in Adversarial Environments—0
Security Orchestration, Automation, and Response Engine for Deployment of Behavioural Honeypots—0
Segmented Federated Learning for Adaptive Intrusion Detection System—0
Sensor Scheduling in Intrusion Detection Games with Uncertain Payoffs—0
Sequential Binary Classification for Intrusion Detection—0
SFE-GACN: A Novel Unknown Attack Detection Method Using Intra Categories Generation in Embedding Space—0
Shallow and Deep Networks Intrusion Detection System: A Taxonomy and Survey—0
Should I Raise The Red Flag? A comprehensive survey of anomaly scoring methods toward mitigating false alarms—0
SIGMA : Strengthening IDS with GAN and Metaheuristics Attacks—0
Simultaneous Intrusion Detection and Localization Using ISAC Network—0
Smart Water Security with AI and Blockchain-Enhanced Digital Twins—0
SoK: Applying Machine Learning in Security - A Survey—0
SoK: Knowledge is All You Need: Accelerating Last Mile Delivery for Automated Provenance-based Intrusion Detection with LLMs—0
SoK: Realistic Adversarial Attacks and Defenses for Intelligent Network Intrusion Detection—0
SOME/IP Intrusion Detection using Deep Learning-based Sequential Models in Automotive Ethernet Networks—0
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
Show:102550
← PrevPage 12 of 16Next →
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