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
Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach—0
Intrusion detection in computer systems by using artificial neural networks with Deep Learning approaches—0
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
Review: Deep Learning Methods for Cybersecurity and Intrusion Detection Systems—0
Intrusion Detection Systems for IoT: opportunities and challenges offered by Edge Computing and Machine Learning—0
Training a quantum annealing based restricted Boltzmann machine on cybersecurity data—0
Omni: Automated Ensemble with Unexpected Models against Adversarial Evasion Attack—0
Enhanced Few-shot Learning for Intrusion Detection in Railway Video Surveillance—0
Adversarial Examples in Constrained Domains—0
Unsupervised Intrusion Detection System for Unmanned Aerial Vehicle with Less Labeling Effort—0
DualNet: Locate Then Detect Effective Payload with Deep Attention Network—0
On the Usage of Generative Models for Network Anomaly Detection in Multivariate Time-Series—0
DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture—0
Spiking Neural Networks with Single-Spike Temporal-Coded Neurons for Network Intrusion Detection—0
Interpretable Sequence Classification via Discrete OptimizationCode0
The Effective Methods for Intrusion Detection With Limited Network Attack Data: Multi-Task Learning and Oversampling—0
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed NetworksCode0
Graph-Based Intrusion Detection System for Controller Area Networks—0
Experimental Review of Neural-based approaches for Network Intrusion Management—0
Machine Learning Applications in Misuse and Anomaly Detection—0
PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection—0
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion DetectionCode0
Network Intrusion Detection Using Wrapper-based Decision Tree for Feature Selection—0
Multi-Stage Optimized Machine Learning Framework for Network Intrusion Detection—0
Bayesian Optimization with Machine Learning Algorithms Towards Anomaly 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