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 451–500 of 800 papers

TitleStatusHype
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
Collaborative Learning for Cyberattack Detection in Blockchain Networks—0
FGAN: Federated Generative Adversarial Networks for Anomaly Detection in Network Traffic—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
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
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
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
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
A Hybrid Approach for an Interpretable and Explainable Intrusion Detection System—0
A Dynamic Watermarking Algorithm for Finite Markov Decision Problems—0
A Cyber Threat Intelligence Sharing Scheme based on Federated Learning for Network Intrusion Detection—0
Intrusion Detection: Machine Learning Baseline Calculations for Image Classification—0
A Comparative Analysis of Machine Learning Algorithms for Intrusion Detection in Edge-Enabled IoT Networks—0
Intrusion Detection using Spatial-Temporal features based on Riemannian Manifold—0
Orthogonal variance-based feature selection for intrusion detection systems—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
PWG-IDS: An Intrusion Detection Model for Solving Class Imbalance in IIoT Networks Using Generative Adversarial Networks—0
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data—0
Automating Privilege Escalation with Deep Reinforcement Learning—0
From Zero-Shot Machine Learning to Zero-Day Attack Detection—0
Evaluating the Robustness of Time Series Anomaly and Intrusion Detection Methods against Adversarial Attacks—0
LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach—0
A Novel Online Incremental Learning Intrusion Prevention System—0
Modern Cybersecurity Solution using Supervised Machine Learning—0
Integrating Sensing and Communication in Cellular Networks via NR Sidelink—0
Intrusion Detection using Network Traffic Profiling and Machine Learning for IoT—0
Feature Analysis for Machine Learning-based IoT Intrusion Detection—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