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 726–750 of 800 papers

TitleStatusHype
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