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 641–650 of 800 papers

Title	Date	Tasks	Status	Hype
Machine Learning Based Network Vulnerability Analysis of Industrial Internet of Things	Nov 13, 2019	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems	Nov 8, 2019	Adversarial AttackIntrusion Detection	—Unverified	0
AutoIDS: Auto-encoder Based Method for Intrusion Detection System	Nov 8, 2019	DecoderIntrusion Detection	—Unverified	0
The Threat of Adversarial Attacks on Machine Learning in Network Security -- A Survey	Nov 6, 2019	BIG-bench Machine LearningClassification	—Unverified	0
Investigating Resistance of Deep Learning-based IDS against Adversaries using min-max Optimization	Oct 30, 2019	Adversarial AttackIntrusion Detection	—Unverified	0
Intrusion Detection using Sequential Hybrid Model	Oct 26, 2019	Anomaly DetectionIntrusion Detection	—Unverified	0
ASNM Datasets: A Collection of Network Traffic Features for Testing of Adversarial Classifiers and Network Intrusion Detectors	Oct 23, 2019	Intrusion DetectionNetwork Intrusion Detection	—Unverified	0
Tree-based Intelligent Intrusion Detection System in Internet of Vehicles	Oct 18, 2019	Autonomous VehiclesEnsemble Learning	CodeCode Available	1
Kernel density estimation based sampling for imbalanced class distribution	Oct 17, 2019	Density EstimationFraud Detection	—Unverified	0
WOTBoost: Weighted Oversampling Technique in Boosting for imbalanced learning	Oct 17, 2019	Fraud DetectionGeneral Classification	—Unverified	0

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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