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 71–80 of 800 papers

Title	Date	Tasks	Status	Hype
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks	Feb 10, 2020	Anomaly DetectionAttribute	CodeCode Available	1
SparseIDS: Learning Packet Sampling with Reinforcement Learning	Feb 10, 2020	Computational EfficiencyEdge-computing	CodeCode Available	1
Cyber Attack Detection thanks to Machine Learning Algorithms	Jan 17, 2020	BIG-bench Machine LearningCyber Attack Detection	CodeCode Available	1
Explainability and Adversarial Robustness for RNNs	Dec 20, 2019	Adversarial RobustnessFeature Importance	CodeCode Available	1
Tree-based Intelligent Intrusion Detection System in Internet of Vehicles	Oct 18, 2019	Autonomous VehiclesEnsemble Learning	CodeCode Available	1
CyberRAG: An agentic RAG cyber attack classification and reporting tool	Jul 3, 2025	Intrusion DetectionRAG	—Unverified	0
Detection of Cyber Attack in Network using Machine Learning Techniques.	Jul 2, 2025	Intrusion Detection	—Unverified	0
Generative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks	Jun 26, 2025	Anomaly DetectionGenerative Adversarial Network	—Unverified	0
Poster: Enhancing GNN Robustness for Network Intrusion Detection via Agent-based Analysis	Jun 25, 2025	Adversarial AttackIntrusion Detection	—Unverified	0
KnowML: Improving Generalization of ML-NIDS with Attack Knowledge Graphs	Jun 24, 2025	Intrusion DetectionKnowledge Graphs	—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