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

References (51)

  1. 1 Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization [DOI]
  2. 2 Survey of intrusion detection systems: techniques, datasets and challenges [DOI]
  3. 3 Testing Intrusion detection systems [DOI]
  4. 4 Deep learning for cyber security intrusion detection: Approaches, datasets, and comparative study [DOI]
  5. 5 A survey of network-based intrusion detection data sets [DOI]
  6. 6 An Adaptive Ensemble Machine Learning Model for Intrusion Detection [DOI]
  7. 7 Intrusion detection model using fusion of chi-square feature selection and multi class SVM [DOI]
  8. 8 A Detailed Analysis on NSL-KDD Dataset Using Various Machine Learning Techniques for Intrusion Detection
  9. 9 Increasing the Performance of Machine Learning-Based IDSs on an Imbalanced and Up-to-Date Dataset [DOI]
  10. 10 A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems [DOI]
  11. 11 Intrusion detection model using machine learning algorithm on Big Data environment [DOI]
  12. 12 Intrusion Detection Using Big Data and Deep Learning Techniques [DOI]
  13. 13 An Evaluation Framework for Intrusion Detection Dataset [DOI]
  14. 14 Dynamic Network Anomaly Detection System by Using Deep Learning Techniques [DOI]
  15. 15 Network intrusion detection system using J48 Decision Tree [DOI]
  16. 16 A Scalable and Hybrid Intrusion Detection System Based on the Convolutional-LSTM Network [DOI]
  17. 17 A novel scalable intrusion detection system based on deep learning [DOI]
  18. 18 LSTM for Anomaly-Based Network Intrusion Detection [DOI]
  19. 19 Statistical analysis of CIDDS-001 dataset for Network Intrusion Detection Systems using Distance-based Machine Learning [DOI]
  20. 20 Distributed Intrusion Detection System for Cloud Environments based on Data Mining techniques [DOI]
  21. 21 Analysis of Support Vector Machine-based Intrusion Detection Techniques [DOI]
  22. 22 An efficient intrusion detection technique based on support vector machine and improved binary gravitational search algorithm [DOI]
  23. 23 Implementation of adaptive scheme in evolutionary technique for anomaly-based intrusion detection [DOI]
  24. 24 An intelligent intrusion detection system [DOI]
  25. 25 Classification of Attack Types for Intrusion Detection Systems Using a Machine Learning Algorithm [DOI]
  26. 26 Network Intrusion Detection Using Clustering and Gradient Boosting [DOI]
  27. 27 Machine and Deep Learning Based Comparative Analysis Using Hybrid Approaches for Intrusion Detection System [DOI]
  28. 28 Anomaly Based Intrusion Detection Using Hybrid Learning Approach of Combining k-Medoids Clustering and Naïve Bayes Classification [DOI]
  29. 29 Intensive Pre-Processing of KDD Cup 99 for Network Intrusion Classification Using Machine Learning Techniques [DOI]
  30. 30 A deep learning method to detect network intrusion through flow‐based features [DOI]
  31. 31 A Case Study on using Deep Learning for Network Intrusion Detection [DOI]
  32. 32 Effectiveness of Machine Learning Based Intrusion Detection Systems [DOI]
  33. 33 An improved ensemble approach for effective intrusion detection [DOI]
  34. 34 Unknown Attacks Detection Using Feature Extraction from Anomaly-Based IDS Alerts [DOI]
  35. 35 Performances of Machine Learning Algorithms for Binary Classification of Network Anomaly Detection System [DOI]
  36. 36 Survey on Intrusion Detection System Types
  37. 37 Deep Learning Based Latent Feature Extraction for Intrusion Detection [DOI]
  38. 38 Hybrid Approach for Intrusion Detection System [DOI]
  39. 39 Study of long short-term memory in flow-based network intrusion detection system [DOI]
  40. 40 A Real Time Adaptive Intrusion Detection Alert Classifier for High Speed Networks [DOI]
  41. 41 Cooperation of Intelligent Honeypots to Detect Unknown Malicious Codes [DOI]
  42. 42 Efficient algorithm for intrusion attack classification by analyzing KDD Cup 99 [DOI]
  43. 43 Ensemble Feature Selection for Improving Intrusion Detection Classification Accuracy [DOI]
  44. 44 Combination of Multi Classification Algorithms for Intrusion Detection System
  45. 45 Multi Class Machine Learning Algorithms for Intrusion Detection - A Performance Study [DOI]
  46. 46 A design of a parallel network anomaly detection algorithm based on classification [DOI]
  47. 47 Performance Analysis of various classifiers using Benchmark Datasets in Weka tools [DOI]
  48. 48 Distributed Online Averaged One Dependence Estimator (DOAODE) Algorithm for Multi-class Classification of Network Anomaly Detection System [DOI]
  49. 49 Statistical analysis of honeypot data and building of Kyoto 2006+ dataset for NIDS evaluation [DOI]
  50. 50 On evaluation of Network Intrusion Detection Systems: Statistical analysis of CIDDS-001 dataset using Machine Learning Techniques [DOI]
  51. 51 Evaluation of Network Intrusion Detection with Features Selection and Machine Learning Algorithms on CICIDS-2017 Dataset [DOI]

Cited by 200

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

Showing 101 of 200 known citations — external sources report more than can currently be individually listed.

Related research

Article metrics

Real usage data collected on this platform.

92

Page views

0

PDF downloads

4

Outbound clicks

200

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.