Design an Intrusion Detection System based on Feature Selection Using ML Algorithms

Authors

  • Srinath Venkatesan,

DOI:

https://doi.org/10.17762/msea.v72i1.2000

Abstract

With the development of the Internet and Technology, cyber-attacks are growing rapidly thus cyber security is an important aspect which needs to be given attention. There are several types of attacks occurring across the internet like: Denial of Service (DoS) , R2L (Root to Local attacks), U2R (User to Root attack), Probe (Probing attacks) , DNS Spoofing attack etc  for which ways to identify the vulnerabilities have to be found. Machine learning, an emerging field in the current time has many techniques that can be adopted in various domains where it shows its dominance over many traditional algorithms. These techniques can be exploited in the field of cyber security with the aim of detecting intrusions which can support or even replace the first level of security analysts. This paper aims in building a model for intrusion detection by applying various machine learning algorithms on the selected features obtained through the modelling process. NSL-KDD dataset is used in order to build IDS. The paper provides a brief description of various machine learning algorithms like SVM, Random Forests, Decision Tree  in order to study the of attacks  on the prescribed  dataset using the selected critical features which  aims in improving the accuracy. The paper also provides a comparative study of the algorithms used to determine the algorithm that works the best.

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Published

2023-02-18

How to Cite

Srinath Venkatesan,. (2023). Design an Intrusion Detection System based on Feature Selection Using ML Algorithms . Mathematical Statistician and Engineering Applications, 72(1), 702–710. https://doi.org/10.17762/msea.v72i1.2000

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Section

Articles