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 611–620 of 800 papers

Title	Date	Tasks	Status	Hype
SFE-GACN: A Novel Unknown Attack Detection Method Using Intra Categories Generation in Embedding Space	Apr 12, 2020	Data AugmentationGenerative Adversarial Network	—Unverified	0
Adversarial Attacks on Machine Learning Cybersecurity Defences in Industrial Control Systems	Apr 10, 2020	BIG-bench Machine LearningGeneral Classification	—Unverified	0
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications	Apr 6, 2020	Intrusion DetectionNetwork Intrusion Detection	CodeCode Available	1
ReRe: A Lightweight Real-time Ready-to-Go Anomaly Detection Approach for Time Series	Apr 5, 2020	Anomaly DetectionIntrusion Detection	—Unverified	0
IMPACT: Impersonation Attack Detection via Edge Computing Using Deep Autoencoder and Feature Abstraction	Apr 2, 2020	Edge-computingIntrusion Detection	—Unverified	0
Hybrid Model For Intrusion Detection Systems	Mar 19, 2020	Intrusion Detectionmodel	—Unverified	0
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection	Mar 11, 2020	Dimensionality ReductionFraud Detection	CodeCode Available	1
Machine Learning based Anomaly Detection for 5G Networks	Mar 7, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Securing of Unmanned Aerial Systems (UAS) against security threats using human immune system	Mar 1, 2020	Intrusion Detection	—Unverified	0
1D CNN Based Network Intrusion Detection with Normalization on Imbalanced Data	Mar 1, 2020	BIG-bench Machine LearningIntrusion Detection	—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