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 631–640 of 800 papers

Title	Date	Tasks	Status	Hype
A Performance Comparison of Data Mining Algorithms Based Intrusion Detection System for Smart Grid	Dec 31, 2019	Intrusion Detection	—Unverified	0
A Robust Comparison of the KDDCup99 and NSL-KDD IoT Network Intrusion Detection Datasets Through Various Machine Learning Algorithms	Dec 31, 2019	Intrusion DetectionNetwork Intrusion Detection	—Unverified	0
Explainability and Adversarial Robustness for RNNs	Dec 20, 2019	Adversarial RobustnessFeature Importance	CodeCode Available	1
SIGMA : Strengthening IDS with GAN and Metaheuristics Attacks	Dec 18, 2019	BIG-bench Machine LearningIntrusion Detection	—Unverified	0
Hardening Random Forest Cyber Detectors Against Adversarial Attacks	Dec 9, 2019	BIG-bench Machine LearningIntrusion Detection	—Unverified	0
Detecting Cyberattacks in Industrial Control Systems Using Online Learning Algorithms	Dec 8, 2019	continuous-controlContinuous Control	—Unverified	0
An Attribute Oriented Induction based Methodology for Data Driven Predictive Maintenance	Dec 2, 2019	Anomaly DetectionAttribute	—Unverified	0
Network Intrusion Detection based on LSTM and Feature Embedding	Nov 26, 2019	Binary ClassificationIntrusion Detection	—Unverified	0
Host-based anomaly detection using Eigentraces feature extraction and one-class classification on system call trace data	Nov 25, 2019	Anomaly DetectionIntrusion Detection	—Unverified	0
Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response	Nov 22, 2019	Explainable artificial intelligenceIntrusion 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