UNR-IDD: Intrusion Detection Dataset using Network Port Statistics
Tapadhir Das, Osama Abu Hamdan, Raj Mani Shukla, Shamik Sengupta, Engin Arslan
Abstract
Multiple datasets have been proposed to create Machine Learning (ML)-based Network Intrusion Detection Systems (NIDS). However, many of these datasets suffer from sub-optimal performance and inadequate tail class representation. In this paper, we propose the University of Nevada - Reno Intrusion Detection Dataset (UNR-IDD), which utilizes network port statistics for fine-grained analysis of intrusions. Evaluation results show that UNR-IDD is better than existing NIDS datasets with an Fμ, score of 94% and a minimum F-score of 86%. This is mainly because of sufficient and equal representation of various anomaly types in the UNR-IDD dataset.
Topics & Concepts
Intrusion detection systemComputer scienceRepresentation (politics)IntrusionData miningPort (circuit theory)Class (philosophy)Artificial intelligenceMachine learningEngineeringGeochemistryPoliticsElectrical engineeringPolitical scienceLawGeologyNetwork Security and Intrusion DetectionInternet Traffic Analysis and Secure E-votingAdvanced Malware Detection Techniques