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 431–440 of 800 papers

Title	Date	Tasks	Status	Hype
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection	Feb 2, 2022	Anomaly DetectionIntrusion Detection	—Unverified	0
Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks	Jan 31, 2022	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles	Jan 27, 2022	Autonomous VehiclesCyber Attack Detection	CodeCode Available	2
Early Detection of Network Attacks Using Deep Learning	Jan 27, 2022	Deep Learningfeature selection	—Unverified	0
One-Shot Learning on Attributed Sequences	Jan 23, 2022	Intrusion DetectionNetwork Intrusion Detection	—Unverified	0
Security Orchestration, Automation, and Response Engine for Deployment of Behavioural Honeypots	Jan 14, 2022	Intrusion DetectionManagement	—Unverified	0
An Interpretable Federated Learning-based Network Intrusion Detection Framework	Jan 10, 2022	Federated LearningIntrusion Detection	—Unverified	0
Feature Selection-based Intrusion Detection System Using Genetic Whale Optimization Algorithm and Sample-based Classification	Jan 3, 2022	feature selectionIntrusion Detection	—Unverified	0
Detect & Reject for Transferability of Black-box Adversarial Attacks Against Network Intrusion Detection Systems	Dec 22, 2021	BIG-bench Machine LearningIntrusion Detection	—Unverified	0
Protocol Based Deep Intrusion Detection for DoS and DDoS attacks using UNSW-NB15 and Bot-IoT data-sets	Dec 20, 2021	Intrusion Detection	CodeCode Available	1

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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