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 691–700 of 800 papers

Title	Date	Tasks	Status
Rallying Adversarial Techniques against Deep Learning for Network Security	Mar 27, 2019	BIG-bench Machine LearningDeep Learning	—Unverified
Probabilistic Modeling for Novelty Detection with Applications to Fraud Identification	Mar 5, 2019	DecoderFraud Detection	—Unverified
A comparative evaluation of novelty detection algorithms for discrete sequences	Feb 28, 2019	Fraud DetectionIntrusion Detection	—Unverified
Securing Fog-to-Things Environment Using Intrusion Detection System Based On Ensemble Learning	Jan 30, 2019	Anomaly DetectionEnsemble Learning	—Unverified
Deep Adversarial Learning in Intrusion Detection: A Data Augmentation Enhanced Framework	Jan 23, 2019	Data AugmentationIntrusion Detection	—Unverified
A short review on Applications of Deep learning for Cyber security	Dec 15, 2018	Android Malware DetectionBIG-bench Machine Learning	—Unverified
Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection	Dec 11, 2018	Anomaly DetectionIntrusion Detection	—Unverified
Attentional Heterogeneous Graph Neural Network: Application to Program Reidentification	Dec 10, 2018	Graph ClassificationGraph Embedding	—Unverified
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks	Dec 7, 2018	Anomaly DetectionDiagnostic	—Unverified
Cyber Anomaly Detection Using Graph-node Role-dynamics	Dec 6, 2018	Anomaly DetectionIntrusion 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