A Survey Current Datasets used for Intrusion Detection using Machine Learning

Authors

  • Ammar Moustafa Tishreen University
  • Mohammed Hejazieh Tishreen University

Keywords:

Intrusion Detection system, Machine Learning, Cyber Attacks, UNSW-NB15 Data Set, CICIDS2018 Data Set, CICIDS2017 Data Set, DARPA Data Set, NSL-KDD Data Set, KDD’99Data Set, ADFA-IDS Data Set

Abstract

Cyberattacks in today's digital age cause the loss of sensitive data and a huge financial loss for enterprises and countries. Therefore, the role of the cyber security expert is very important to protect data from increased and new attacks. Researchers focus on anomaly-based intrusion detection systems to detect these unknown attacks and machine learning algorithms play a vital role in this process because they detect attacks accurately. Data sets currently used in intrusion detection systems suffer from a clear lack of real network threats, attack representation, and include a large number of abandoned threats, which limit the accuracy of detection within the current intrusion detection systems' methods of machine learning, which make them unable to trace increasing and new attacks in cloud environments, containers. This research paper aims to combine classification and analysis of existing data sets in order to improve the creation new data sets that simulate the actual reality of the network's real data. This will improve the efficiency of the next generation of intrusion detection systems and reflect network threats more accurately

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Published

2022-11-17

How to Cite

1.
عمار مصطفى, محمد حجازية. A Survey Current Datasets used for Intrusion Detection using Machine Learning . Tuj-eng [Internet]. 2022Nov.17 [cited 2024May2];44(5):167-81. Available from: https://journal.tishreen.edu.sy/index.php/engscnc/article/view/13167

Issue

Vol. 44 No. 5 (2022): Tishreen University Journal of Research and Scientific Studies - Engineering Sciences Series

Section

Articles

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Copyright (c) 2022 ttps://creativecommons.org/licenses/by-nc-sa/4.0/

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