Skip to content
Research Article Open access CC BY 4.0

A Detailed Analysis of Benchmark Datasets for Network Intrusion Detection System

Mossa Ghurab, Ghaleb Gaphari, Faisal Alshami, Reem Alshamy, Suad Othman

Asian Journal of Research in Computer Science · pp. 14–33 · Published 14 Apr 2021

10.9734/ajrcos/2021/v7i430185

Abstract

The enormous increase in the use of the Internet in daily life has provided an opportunity for the intruder attempt to compromise the security principles of availability, confidentiality, and integrity. As a result, organizations are working to increase the level of security by using attack detection techniques such as Network Intrusion Detection System (NIDS), which monitors and analyzes network flow and attacks detection. There are a lot of researches proposed to develop the NIDS and depend on the dataset for the evaluation. Datasets allow evaluating the ability in detecting intrusion behavior. This paper introduces a detailed analysis of benchmark and recent datasets for NIDS. Specifically, we describe eight well-known datasets that include: KDD99, NSL-KDD, KYOTO 2006+, ISCX2012, UNSW-NB 15, CIDDS-001, CICIDS2017, and CSE-CIC-IDS2018. For each dataset, we provide a detailed analysis of its instances, features, classes, and the nature of the features. The main objective of this paper is to offer overviews of the datasets are available for the NIDS and what each dataset is comprised of. Furthermore, some recommendations were made to use network-based datasets.

KDD99 NSL-KDD KYOTO 2006 ISCX2012 UNSW-NB 15 CIDDS-001 CICIDS2017 CSE-CIC-IDS2018

Cited by 101

Detection of HTTP DDoS Attacks Using NFStream and TensorFlow

M. Chovanec, M. Hasin, Martin Havrilla · Applied Sciences · 2023

Anomaly Detection Dataset for Industrial Control Systems

Alireza Dehlaghi-Ghadim, Mahshid Helali Moghadam, Ali Balador · IEEE Access · 2023

Cyber Security Threats and Countermeasures using Machine and Deep Learning Approaches: A Survey

M. M., Venkatesh, V. R. · Journal of Computer Science · 2023

A New IDS for Detecting DDoS Attacks in Wireless Networks using Spotted Hyena Optimization and Fuzzy Temporal CNN

C. M. Nalayini, Jeevaa Katiravan C. M. Nalayini · 網際網路技術學刊 · 2023

Network-based Intrusion Detection Datasets: A Survey

L. Ahmed, Y. A. M. Hamad, A. M. Abdalla · Automation, Control, and Information Technology · 2022

Effects of Un targeted Adversarial Attacks on Deep Learning Methods

Elif Değirmenci, Ilker Ozcelik, A. Yazıcı · 2022 15th International Conference on Information Security and Cryptography (ISCTURKEY) · 2022

A Survey on Intrusion Detection Systems: Types, Datasets, Machine Learning methods for NIDS and Challenges

Supongmen Walling, Sibesh Lodh · International Conference on Computing Communication and Networking Technologies · 2022

Robust Enhancement of Intrusion Detection Systems Using Deep Reinforcement Learning and Stochastic Game

Hafsa Benaddi, Khalil Ibrahimi, A. Benslimane · IEEE Transactions on Vehicular Technology · 2022

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research

Zhibo Zhang, Hussam M. N. Al Hamadi, E. Damiani · IEEE Access · 2022

Enabling intrusion detection systems with dueling double deep Q-learning

Y. Badr · Digital Transformation and Society · 2022

Related research

Article metrics

Real usage data collected on this platform.

85

Page views

0

PDF downloads

4

Outbound clicks

101

Citations

Views over time

Views by country

Approximate, from request IP at view time — not citizenship or institution. Countries with fewer than 5 views are grouped as "Other".

Traffic sources

Referring site, by host.

Views and downloads exclude known bots/crawlers. Citations combines this platform's own DOI-resolved index with each external source's own reported total — see Cited by above for individually listed citing works. Last refreshed 0 seconds ago.