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 626–650 of 800 papers

TitleStatusHype
RePAD: Real-time Proactive Anomaly Detection for Time Series—0
Pelican: A Deep Residual Network for Network Intrusion Detection—0
Cyber Attack Detection thanks to Machine Learning AlgorithmsCode1
A Content-Based Deep Intrusion Detection System—0
Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure—0
A Performance Comparison of Data Mining Algorithms Based Intrusion Detection System for Smart Grid—0
A Robust Comparison of the KDDCup99 and NSL-KDD IoT Network Intrusion Detection Datasets Through Various Machine Learning Algorithms—0
Explainability and Adversarial Robustness for RNNsCode1
SIGMA : Strengthening IDS with GAN and Metaheuristics Attacks—0
Hardening Random Forest Cyber Detectors Against Adversarial Attacks—0
Detecting Cyberattacks in Industrial Control Systems Using Online Learning Algorithms—0
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance—0
Network Intrusion Detection based on LSTM and Feature Embedding—0
Host-based anomaly detection using Eigentraces feature extraction and one-class classification on system call trace data—0
Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response—0
Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things—0
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems—0
AutoIDS: Auto-encoder Based Method for Intrusion Detection System—0
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey—0
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization—0
Intrusion Detection using Sequential Hybrid Model—0
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors—0
Tree-based Intelligent Intrusion Detection System in Internet of VehiclesCode1
Kernel density estimation based sampling for imbalanced class distribution—0
WOTBoost: Weighted Oversampling Technique in Boosting for imbalanced learning—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