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 701–750 of 800 papers

TitleStatusHype
AI-based Two-Stage Intrusion Detection for Software Defined IoT Networks—0
AIDPS:Adaptive Intrusion Detection and Prevention System for Underwater Acoustic Sensor Networks—0
AI-Driven Chatbot for Intrusion Detection in Edge Networks: Enhancing Cybersecurity with Ethical User Consent—0
AI-Driven Intrusion Detection Systems (IDS) on the ROAD Dataset: A Comparative Analysis for Automotive Controller Area Network (CAN)—0
A Life-long Learning Intrusion Detection System for 6G-Enabled IoV—0
A Lightweight FPGA-based IDS-ECU Architecture for Automotive CAN—0
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method—0
A Lightweight Multi-Attack CAN Intrusion Detection System on Hybrid FPGAs—0
A Machine Learning based Empirical Evaluation of Cyber Threat Actors High Level Attack Patterns over Low level Attack Patterns in Attributing Attacks—0
A Machine Learning Based Intrusion Detection System for Software Defined 5G Network—0
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics—0
A model for multi-attack classification to improve intrusion detection performance using deep learning approaches—0
A Modern Analysis of Aging Machine Learning Based IoT Cybersecurity Methods—0
A multiagent based framework secured with layered SVM-based IDS for remote healthcare systems—0
An Adaptable Deep Learning-Based Intrusion Detection System to Zero-Day Attacks—0
An Adversarial Approach for Explainable AI in Intrusion Detection Systems—0
An Adversarial Robustness Benchmark for Enterprise Network Intrusion Detection—0
Analysis of Intelligent Classifiers and Enhancing the Detection Accuracy for Intrusion Detection System—0
Analysis of Zero Day Attack Detection Using MLP and XAI—0
Analyzing Adversarial Attacks Against Deep Learning for Intrusion Detection in IoT Networks—0
Analyzing and Storing Network Intrusion Detection Data using Bayesian Coresets: A Preliminary Study in Offline and Streaming Settings—0
An Anomaly Detection System Based on Generative Classifiers for Controller Area Network—0
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance—0
An AutoML-based approach for Network Intrusion Detection—0
An Autonomous Intrusion Detection System Using an Ensemble of Advanced Learners—0
An Effective Networks Intrusion Detection Approach Based on Hybrid Harris Hawks and Multi-Layer Perceptron—0
An Efficient Anomaly Detection Approach using Cube Sampling with Streaming Data—0
Building an Efficient Intrusion Detection System Based on Feature Selection and Ensemble Classifier—0
An empirical evaluation for the intrusion detection features based on machine learning and feature selection methods—0
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System—0
A Network Intrusions Detection System based on a Quantum Bio Inspired Algorithm—0
A New Clustering Approach for Anomaly Intrusion Detection—0
A New Intrusion Detection System using the Improved Dendritic Cell Algorithm—0
A new semi-supervised inductive transfer learning framework: Co-Transfer—0
An Experimental Analysis of Attack Classification Using Machine Learning in IoT Networks—0
Evaluating Standard Feature Sets Towards Increased Generalisability and Explainability of ML-based Network Intrusion Detection—0
An Identification System Using Eye Detection Based On Wavelets And Neural Networks—0
An incremental hybrid adaptive network-based IDS in Software Defined Networks to detect stealth attacks—0
An Intelligent Mechanism for Monitoring and Detecting Intrusions in IoT Devices—0
An Interpretable Federated Learning-based Network Intrusion Detection Framework—0
An Interpretable Generalization Mechanism for Accurately Detecting Anomaly and Identifying Networking Intrusion Techniques—0
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
Anomaly based network intrusion detection for IoT attacks using deep learning technique—0
Anomaly Detection Dataset for Industrial Control Systems—0
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection—0
Cybersecurity Anomaly Detection in Adversarial Environments—0
Anomaly Detection in Intra-Vehicle Networks—0
Anomaly detection in wide area network mesh using two machine learning anomaly detection algorithms—0
Anomaly detection optimization using big data and deep learning to reduce false-positive—0
Anomaly Detection via Federated 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