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 551–560 of 800 papers

Title	Date	Tasks	Status	Hype
A Novel Resampling Technique for Imbalanced Dataset Optimization	Dec 30, 2020	Intrusion DetectionMalware Detection	—Unverified	0
A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset	Dec 29, 2020	Anomaly DetectionBenchmarking	—Unverified	0
Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On Autoencoder	Dec 27, 2020	Anomaly DetectionIntrusion Detection	CodeCode Available	0
Assessment of the Relative Importance of different hyper-parameters of LSTM for an IDS	Dec 26, 2020	Intrusion DetectionLanguage Modeling	—Unverified	0
Fragments Expert A Graphical User Interface MATLAB Toolbox for Classification of File Fragments	Dec 23, 2020	ClassificationComputer Security	CodeCode Available	0
RNNIDS: Enhancing Network Intrusion Detection Systems through Deep Learning	Dec 21, 2020	Deep LearningIntrusion Detection	—Unverified	0
Unsupervised Anomaly Detectors to Detect Intrusions in the Current Threat Landscape	Dec 21, 2020	Anomaly DetectionBinary Classification	—Unverified	0
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated Learning	Dec 16, 2020	Federated LearningIntrusion Detection	CodeCode Available	1
Detecting Botnet Attacks in IoT Environments: An Optimized Machine Learning Approach	Dec 16, 2020	Bayesian OptimizationBIG-bench Machine Learning	—Unverified	0
Intrusion detection in computer systems by using artificial neural networks with Deep Learning approaches	Dec 15, 2020	Intrusion 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