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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 431–440 of 800 papers

Title	Date	Tasks	Status
Machine Learning based Anomaly Detection for 5G Networks	Mar 7, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning-Based Intrusion Detection: Feature Selection versus Feature Extraction	Jul 4, 2023	feature selectionIntrusion Detection	—Unverified
Machine Learning-Based Intrusion Detection and Prevention System for IIoT Smart Metering Networks: Challenges and Solutions	Feb 16, 2025	Decision MakingIntrusion Detection	—Unverified
Machine learning-based network intrusion detection for big and imbalanced data using oversampling, stacking feature embedding and feature extraction	Jan 22, 2024	BlockingDimensionality Reduction	—Unverified
Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things	Nov 13, 2019	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats	Apr 7, 2022	Intrusion Detection	—Unverified
Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments	Oct 23, 2018	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning for Intrusion Detection in Industrial Control Systems: Applications, Challenges, and Recommendations	Feb 24, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Machine Learning Techniques for Intrusion Detection	Dec 8, 2013	BIG-bench Machine LearningIntrusion Detection	—Unverified
Manifold regularization based on Nyström type subsampling	Oct 13, 2017	image-classificationImage Classification	—Unverified

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All datasets CICIDS2017 CIC-DDoS CIC-DoS UNSW-NB15

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	Random Forest	Accuracy (%)	98.13	—	Unverified
2	K-Nearest Neighbors	Accuracy (%)	98.07	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MSTREAM-PCA	AUC	0.94	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MSTREAM-IB	AUC	0.95	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MSTREAM-AE	AUC	0.9	—	Unverified