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 601–650 of 800 papers

TitleStatusHype
EagerNet: Early Predictions of Neural Networks for Computationally Efficient Intrusion DetectionCode0
A Comparative Study of AI-based Intrusion Detection Techniques in Critical Infrastructures—0
Fragments-Expert: A Graphical User Interface MATLAB Toolbox for Classification of File Fragments—0
Evaluation of Adversarial Training on Different Types of Neural Networks in Deep Learning-based IDSs—0
NERD: Neural Network for Edict of Risky Data Streams—0
Leveraging Siamese Networks for One-Shot Intrusion Detection Model—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks—0
Learning With Differential Privacy—0
G-IDS: Generative Adversarial Networks Assisted Intrusion Detection System—0
Identifying Vulnerabilities of Industrial Control Systems using Evolutionary Multiobjective Optimisation—0
Data Mining with Big Data in Intrusion Detection Systems: A Systematic Literature Review—0
A cognitive based Intrusion detection system—0
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System—0
Adversarial Machine Learning in Network Intrusion Detection Systems—0
A New Intrusion Detection System using the Improved Dendritic Cell Algorithm—0
Multi-stage Jamming Attacks Detection using Deep Learning Combined with Kernelized Support Vector Machine in 5G Cloud Radio Access Networks—0
SFE-GACN: A Novel Unknown Attack Detection Method Using Intra Categories Generation in Embedding Space—0
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems—0
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series—0
IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction—0
Hybrid Model For Intrusion Detection Systems—0
Machine Learning based Anomaly Detection for 5G Networks—0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data—0
Securing of Unmanned Aerial Systems (UAS) against security threats using human immune system—0
An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners—0
IoT Behavioral Monitoring via Network Traffic Analysis—0
Survey of Network Intrusion Detection Methods from the Perspective of the Knowledge Discovery in Databases Process—0
RePAD: Real-time Proactive Anomaly Detection for Time Series—0
Pelican: A Deep Residual Network for Network Intrusion Detection—0
A Content-Based Deep Intrusion Detection System—0
Deep Learning-Based Intrusion Detection System for Advanced Metering Infrastructure—0
A Robust Comparison of the KDDCup99 and NSL-KDD IoT Network Intrusion Detection Datasets Through Various Machine Learning Algorithms—0
A Performance Comparison of Data Mining Algorithms Based Intrusion Detection System for Smart Grid—0
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
WOTBoost: Weighted Oversampling Technique in Boosting for imbalanced learning—0
Kernel density estimation based sampling for imbalanced class distribution—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