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 31–40 of 800 papers

Title	Date	Tasks	Status	Hype	Score
Efficient Deep CNN-BiLSTM Model for Network Intrusion Detection	Jun 26, 2020	Anomaly DetectionIntrusion Detection	CodeCode Available	1	5
Adaptive Intrusion Detection in the Networking of Large-Scale LANs with Segmented Federated Learning	Dec 16, 2020	Federated LearningIntrusion Detection	CodeCode Available	1	5
Cyber Attack Detection thanks to Machine Learning Algorithms	Jan 17, 2020	BIG-bench Machine LearningCyber Attack Detection	CodeCode Available	1	5
A flow-based IDS using Machine Learning in eBPF	Feb 19, 2021	BIG-bench Machine LearningIntrusion Detection	CodeCode Available	1	5
3D-IDS: Doubly Disentangled Dynamic Intrusion Detection	Jul 2, 2023	DisentanglementFew-Shot Learning	CodeCode Available	1	5
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification	Feb 11, 2022	Intrusion DetectionTraffic Classification	CodeCode Available	1	5
A new Deep Learning Based Intrusion Detection System for Cloud Security	Jun 13, 2021	Cloud ComputingDeep Learning	CodeCode Available	1	5
An Intrusion Detection System based on Deep Belief Networks	Jul 5, 2022	Intrusion DetectionNetwork Intrusion Detection	CodeCode Available	1	5
AnoShift: A Distribution Shift Benchmark for Unsupervised Anomaly Detection	Jun 30, 2022	Intrusion DetectionNetwork Intrusion Detection	CodeCode Available	1	5
Explainability and Adversarial Robustness for RNNs	Dec 20, 2019	Adversarial RobustnessFeature Importance	CodeCode Available	1	5

Show:10 25 50

← PrevPage 4 of 80Next →

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