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

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Showing 501–525 of 800 papers

TitleStatusHype
On the Evaluation of Sequential Machine Learning for Network Intrusion Detection—0
A new Deep Learning Based Intrusion Detection System for Cloud SecurityCode1
Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services—0
A concise method for feature selection via normalized frequencies—0
Sketch-Based Anomaly Detection in Streaming GraphsCode1
FlexParser -- the adaptive log file parser for continuous results in a changing world—0
Network Activities Recognition and Analysis Based on Supervised Machine Learning Classification Methods Using J48 and Naïve Bayes Algorithm—0
MTH-IDS: A Multi-Tiered Hybrid Intrusion Detection System for Internet of VehiclesCode1
Performance Analysis of a Foreground Segmentation Neural Network Model—0
Intrusion Detection System in Smart Home Network Using Bidirectional LSTM and Convolutional Neural Networks Hybrid Model—0
Data Curation and Quality Assurance for Machine Learning-based Cyber Intrusion DetectionCode1
Machine learning on knowledge graphs for context-aware security monitoringCode1
Cybersecurity Anomaly Detection in Adversarial Environments—0
ADASYN-Random Forest Based Intrusion Detection Model—0
Extending Isolation Forest for Anomaly Detection in Big Data via K-Means—0
Launching Adversarial Attacks against Network Intrusion Detection Systems for IoT—0
Robustness of ML-Enhanced IDS to Stealthy Adversaries—0
Adversarial Training for Deep Learning-based Intrusion Detection Systems—0
Benchmarking the Benchmark -- Analysis of Synthetic NIDS Datasets—0
Exploring Cybersecurity Issues in 5G Enabled Electric Vehicle Charging Station with Deep Learning—0
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection—0
A multiagent based framework secured with layered SVM-based IDS for remote healthcare systems—0
Supervised Feature Selection Techniques in Network Intrusion Detection: a Critical Review—0
Performance Evaluation of Machine Learning Techniques for DoS Detection in Wireless Sensor Network—0
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
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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