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 201–225 of 800 papers

TitleStatusHype
Intrusion Detection at Scale with the Assistance of a Command-line Language Model—0
Integrating Graph Neural Networks with Scattering Transform for Anomaly Detection—0
Privacy-Preserving Intrusion Detection using Convolutional Neural Networks—0
Reconfigurable Edge Hardware for Intelligent IDS: Systematic Approach—0
An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks—0
Dealing with Imbalanced Classes in Bot-IoT Dataset—0
A Transformer-Based Framework for Payload Malware Detection and Classification—0
Expectations Versus Reality: Evaluating Intrusion Detection Systems in Practice—0
EG-ConMix: An Intrusion Detection Method based on Graph Contrastive Learning—0
Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems—0
Problem space structural adversarial attacks for Network Intrusion Detection Systems based on Graph Neural NetworksCode1
A Dual-Tier Adaptive One-Class Classification IDS for Emerging Cyberthreats—0
Hierarchical Classification for Intrusion Detection System: Effective Design and Empirical Analysis—0
usfAD Based Effective Unknown Attack Detection Focused IDS Framework—0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems—0
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques—0
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network—0
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural NetworkCode1
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron—0
IT Intrusion Detection Using Statistical Learning and Testbed Measurements—0
MLSTL-WSN: Machine Learning-based Intrusion Detection using SMOTETomek in WSNs—0
On the Cross-Dataset Generalization of Machine Learning for Network Intrusion DetectionCode1
Utilizing Deep Learning for Enhancing Network Resilience in Finance—0
ROSpace: Intrusion Detection Dataset for a ROS2-Based Cyber-Physical SystemCode0
Show:102550
← PrevPage 9 of 32Next →
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