Addressing Challenges in Big Data Intrusion Detection System using Machine Learning Techniques

Authors

  • Almansob SMH Department of Computer Science, Radhai Mahavidyalaya (BAM University), Aurangabad, India
  • Lomte SS Principal, School of Engineering Technology, VDF, Latur, India

DOI:

https://doi.org/10.26438/ijcse/v5i11.127130

Keywords:

Big data, Intrusion Detection System (IDS), Principal Component Analysis (PCA), K-Nearest neighbour (KNN)

Abstract

In the last few years, the number of people around the world is increasing day by day in matching the use of the internet and social media. For this reason, a large volume of data is generated by the internet and social media from gigabytes (GB) to petabytes (PB) with high speed. In this work, it is proposed Intrusion Detection System (IDS) with large amounts of data to address challenges in various types of network attacks using machine learning techniques. On another hand, it is proposed Principal Components Analysis method to reduce high dimensionality and features of data. Therefore, in order to reduce amounts of calculations and improve an accuracy of classification of data. That is, why the use of DARBAI data set in this model and it is applied to K-nearest neighbour method for classification.

References

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Published

2025-11-12
CITATION
DOI: 10.26438/ijcse/v5i11.127130
Published: 2025-11-12

How to Cite

[1]
S. M. H. Almansob and S. S. Lomte, “Addressing Challenges in Big Data Intrusion Detection System using Machine Learning Techniques”, Int. J. Comp. Sci. Eng., vol. 5, no. 11, pp. 127–130, Nov. 2025.

Issue

Vol. 5 No. 11 (2017): IJCSE November Edition

Section

Research Article

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