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 471–480 of 800 papers

Title	Date	Tasks	Status
Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification	Jan 3, 2022	feature selectionIntrusion Detection	—Unverified
Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems	Dec 22, 2021	BIG-bench Machine LearningIntrusion Detection	—Unverified
A Heterogeneous Graph Learning Model for Cyber-Attack Detection	Dec 16, 2021	Cyber Attack DetectionGraph Learning	—Unverified
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)	Dec 14, 2021	Anomaly DetectionExplainable artificial intelligence	—Unverified
Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review	Dec 6, 2021	BIG-bench Machine LearningFeature Engineering	—Unverified
Two-stage Deep Stacked Autoencoder with Shallow Learning for Network Intrusion Detection System	Dec 3, 2021	Feature EngineeringIntrusion Detection	—Unverified
Improving the Reliability of Network Intrusion Detection Systems through Dataset Integration	Dec 2, 2021	Intrusion DetectionNetwork Intrusion Detection	—Unverified
Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks	Dec 2, 2021	Federated LearningIntrusion Detection	—Unverified
Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection	Nov 27, 2021	Intrusion DetectionNetwork Intrusion Detection	CodeCode Available
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection	Nov 25, 2021	Anomaly DetectionBIG-bench Machine Learning	—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