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 41–50 of 800 papers

Title	Date	Tasks	Status	Hype	Score
Explainability and Adversarial Robustness for RNNs	Dec 20, 2019	Adversarial RobustnessFeature Importance	CodeCode Available	1	5
Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network	Mar 3, 2024	Binary ClassificationContrastive Learning	CodeCode Available	1	5
ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks	Feb 15, 2023	Intrusion DetectionSelf-Learning	CodeCode Available	1	5
FedMSE: Federated learning for IoT network intrusion detection	Oct 18, 2024	Federated LearningIntrusion Detection	CodeCode Available	1	5
A Study on Transferability of Deep Learning Models for Network Intrusion Detection	Dec 17, 2023	Data AugmentationIntrusion Detection	CodeCode Available	1	5
Intrusion Detection with Segmented Federated Learning for Large-Scale Multiple LANs	Sep 28, 2020	Federated LearningIntrusion Detection	CodeCode Available	1	5
Bridging the gap to real-world for network intrusion detection systems with data-centric approach	Oct 25, 2021	BIG-bench Machine LearningIntrusion Detection	CodeCode Available	1	5
CAGN-GAT Fusion: A Hybrid Contrastive Attentive Graph Neural Network for Network Intrusion Detection	Mar 2, 2025	FairnessGraph Attention	CodeCode Available	1	5
CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level	May 3, 2022	Intrusion DetectionTime Series	CodeCode Available	1	5
Open-Source Framework for Encrypted Internet and Malicious Traffic Classification	Jun 21, 2022	Intrusion DetectionTraffic Classification	CodeCode Available	1	5

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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