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 426–450 of 800 papers

TitleStatusHype
Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations—0
NetSentry: A Deep Learning Approach to Detecting Incipient Large-scale Network Attacks—0
Survey of Machine Learning Based Intrusion Detection Methods for Internet of Medical Things—0
Trustworthy Anomaly Detection: A Survey—0
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic ClassificationCode1
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection—0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks—0
A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of VehiclesCode2
Early Detection of Network Attacks Using Deep Learning—0
One-Shot Learning on Attributed Sequences—0
Security Orchestration, Automation, and Response Engine for Deployment of Behavioural Honeypots—0
An Interpretable Federated Learning-based Network Intrusion Detection Framework—0
Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification—0
Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems—0
Protocol Based Deep Intrusion Detection for DoS and DDoS attacks using UNSW-NB15 and Bot-IoT data-setsCode1
A Heterogeneous Graph Learning Model for Cyber-Attack Detection—0
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)—0
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review—0
Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System—0
Improving the Reliability of Network Intrusion Detection Systems through Dataset Integration—0
Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks—0
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion DetectionCode0
Graph-based Solutions with Residuals for Intrusion Detection: the Modified E-GraphSAGE and E-ResGAT AlgorithmsCode1
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
Inter-Domain Fusion for Enhanced Intrusion Detection in Power Systems: An Evidence Theoretic and Meta-Heuristic ApproachCode0
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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