Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)

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Performance Comparison of K-Means and t-SNE Data Compression for Intrusion Detection Using HIKARI-2021 Dataset

Authors
Sonam Lowry1, 2, *, Mithlesh Arya3, Surendra Yadav1
1Department of Computer Science and Engineering, Vivekananda Global University, Jaipur, India
2Department of Computer Science and Engineering, JECRC University, Jaipur, India
3Department of Computer Science and Engineering, Swami Keshvanand Institute of Technology, Management & Gramothan, Jaipur, India
*Corresponding author. Email: sonamlowry04@gmail.com
Corresponding Author
Sonam Lowry
Available Online 19 April 2025.
DOI
10.2991/978-94-6463-700-7_28How to use a DOI?
Keywords
HIKARI2021; Machine Learning; Intruder Detection System
Abstract

The problem of safety in the system is a vital as well as sensitive issue. The situation arises for the confidentiality in any institution in addition with that of the personalities too, exclusively it is significant complex data, which is conveyed diagonally to the links. In the outcome, different intrusion finding system is too much dependent on the intricate mechanism. This research paper purposes on stretch on an awareness for comparative evaluation for an unsupervised ML model using the dimensionality reduction technique. The new dataset named HIKARI-2021 is used in the projected IDS model to address the trials related to attack detection in relation to larger dimensionality, implementing innovative approaches for decrease the range, simultaneously improving the proficiency. The result presents that the comparison toward previous learning showed, taking place with the similar datasets, projected architecture achieves improved outcome for the IDS. Therefore, the final outcome is probable anticipated projected structure bids for the better rise in the trust of the security system.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
Series
Advances in Intelligent Systems Research
Publication Date
19 April 2025
ISBN
978-94-6463-700-7
ISSN
1951-6851
DOI
10.2991/978-94-6463-700-7_28How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Sonam Lowry
AU  - Mithlesh Arya
AU  - Surendra Yadav
PY  - 2025
DA  - 2025/04/19
TI  - Performance Comparison of K-Means and t-SNE Data Compression for Intrusion Detection Using HIKARI-2021 Dataset
BT  - Proceedings of the International Conference on Advancements in Computing Technologies and Artificial Intelligence (COMPUTATIA-2025)
PB  - Atlantis Press
SP  - 344
EP  - 353
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-700-7_28
DO  - 10.2991/978-94-6463-700-7_28
ID  - Lowry2025
ER  -