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 701–710 of 800 papers

Title	Date	Tasks	Status
Protection of an information system by artificial intelligence: a three-phase approach based on behaviour analysis to detect a hostile scenario	Dec 3, 2018	Intrusion DetectionManagement	—Unverified
A Machine-Learning Phase Classification Scheme for Anomaly Detection in Signals with Periodic Characteristics	Nov 29, 2018	Anomaly DetectionBIG-bench Machine Learning	—Unverified
An Adversarial Approach for Explainable AI in Intrusion Detection Systems	Nov 28, 2018	BIG-bench Machine LearningIntrusion Detection	—Unverified
OCLEP+: One-class Anomaly and Intrusion Detection Using Minimal Length of Emerging Patterns	Nov 24, 2018	ClassificationGeneral Classification	—Unverified
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives	Nov 13, 2018	BenchmarkingIntrusion Detection	CodeCode Available
Machine Learning for Anomaly Detection and Categorization in Multi-cloud Environments	Oct 23, 2018	Anomaly DetectionBIG-bench Machine Learning	—Unverified
Intrusion Detection Using Mouse Dynamics	Oct 10, 2018	Intrusion Detection	CodeCode Available
Evaluating Shallow and Deep Neural Networks for Network Intrusion Detection Systems in Cyber Security	Oct 8, 2018	BenchmarkingBIG-bench Machine Learning	CodeCode Available
Flow-based Network Traffic Generation using Generative Adversarial Networks	Sep 27, 2018	Image GenerationIntrusion Detection	—Unverified
Time is of the Essence: Machine Learning-based Intrusion Detection in Industrial Time Series Data	Sep 20, 2018	BIG-bench Machine LearningIntrusion Detection	—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