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 401–450 of 800 papers

TitleStatusHype
Explainable and Optimally Configured Artificial Neural Networks for Attack Detection in Smart Homes—0
Many Field Packet Classification with Decomposition and Reinforcement Learning—0
On Generalisability of Machine Learning-based Network Intrusion Detection Systems—0
Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection—0
Anomaly Detection in Intra-Vehicle Networks—0
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-LevelCode1
Federated Semi-Supervised Classification of Multimedia Flows for 3D Networks—0
An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks—0
A review of Federated Learning in Intrusion Detection Systems for IoT—0
Euler: Detecting Network Lateral Movement via Scalable Temporal Link PredictionCode1
STC-IDS: Spatial-Temporal Correlation Feature Analyzing based Intrusion Detection System for Intelligent Connected Vehicles—0
Representation Learning for Content-Sensitive Anomaly Detection in Industrial NetworksCode1
ARLIF-IDS -- Attention augmented Real-Time Isolation Forest Intrusion Detection System—0
Dependable Intrusion Detection System for IoT: A Deep Transfer Learning-based Approach—0
HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection—0
EPASAD: Ellipsoid decision boundary based Process-Aware Stealthy Attack Detector—0
Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats—0
Towards Explainable Meta-Learning for DDoS Detection—0
Effect of Balancing Data Using Synthetic Data on the Performance of Machine Learning Classifiers for Intrusion Detection in Computer Networks—0
IGRF-RFE: A Hybrid Feature Selection Method for MLP-based Network Intrusion Detection on UNSW-NB15 Dataset—0
FGAN: Federated Generative Adversarial Networks for Anomaly Detection in Network Traffic—0
Collaborative Learning for Cyberattack Detection in Blockchain Networks—0
The Cross-evaluation of Machine Learning-based Network Intrusion Detection SystemsCode0
Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection—0
Prepare for Trouble and Make it Double. Supervised and Unsupervised Stacking for AnomalyBased Intrusion Detection—0
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