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 101–125 of 800 papers

TitleStatusHype
Applications of Positive Unlabeled (PU) and Negative Unlabeled (NU) Learning in Cybersecurity—0
Machine Learning-based Android Intrusion Detection System—0
Convolutional Neural Networks and Mixture of Experts for Intrusion Detection in 5G Networks and beyond—0
Optimized IoT Intrusion Detection using Machine Learning Technique—0
Graph-Powered Defense: Controller Area Network Intrusion Detection for Unmanned Aerial Vehicles—0
Swarm Intelligence-Driven Client Selection for Federated Learning in Cybersecurity applications—0
Optimal In-Network Distribution of Learning Functions for a Secure-by-Design Programmable Data Plane of Next-Generation Networks—0
An AutoML-based approach for Network Intrusion Detection—0
The importance of the clustering model to detect new types of intrusion in data traffic—0
Feature Selection for Network Intrusion Detection—0
Take Package as Language: Anomaly Detection Using TransformerCode0
Intelligent Green Efficiency for Intrusion Detection—0
Sdn Intrusion Detection Using Machine Learning Method—0
Securing from Unseen: Connected Pattern Kernels (CoPaK) for Zero-Day Intrusion Detection—0
Enhanced Real-Time Threat Detection in 5G Networks: A Self-Attention RNN Autoencoder Approach for Spectral Intrusion Analysis—0
Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection—0
SCGNet-Stacked Convolution with Gated Recurrent Unit Network for Cyber Network Intrusion Detection and Intrusion Type ClassificationCode0
Implementing Lightweight Intrusion Detection System on Resource Constrained DevicesCode0
A Generative Model Based Honeypot for Industrial OPC UA CommunicationCode0
NIDS Neural Networks Using Sliding Time Window Data Processing with Trainable Activations and its Generalization Capability—0
A Comprehensive Comparative Study of Individual ML Models and Ensemble Strategies for Network Intrusion Detection SystemsCode0
FedMSE: Federated learning for IoT network intrusion detectionCode1
XAI-based Feature Selection for Improved Network Intrusion Detection SystemsCode0
Transforming In-Vehicle Network Intrusion Detection: VAE-based Knowledge Distillation Meets Explainable AI—0
KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph 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