UNR-IDD: Intrusion Detection Dataset using Network Port Statistics

被引：11

作者：

Das, Tapadhir ^{[1
]}

Abu Hamdan, Osama ^{[1
]}

Shukla, Raj Mani ^{[2
]}

Sengupta, Shamik ^{[1
]}

Arslan, Engin ^{[1
]}

机构：

[1] Univ Nevada, Dept Comp Sci & Engn, Reno, NV 89557 USA

[2] Anglia Ruskin Univ, Sch Comp & Informat Sci, Cambridge, England

来源：

2023 IEEE 20TH CONSUMER COMMUNICATIONS & NETWORKING CONFERENCE, CCNC | 2023年

基金：

美国国家科学基金会;

关键词：

Computer networks; network intrusion detection; machine learning; dataset;

D O I：

10.1109/CCNC51644.2023.10059640

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Multiple datasets have been proposed to create Machine Learning (ML)-based Network Intrusion Detection Systems (NIDS). However, many of these datasets suffer from suboptimal 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 F-mu 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.

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