OLGBM: Optuna optimized light gradient boosting machine for intrusion detection

Paper

Arifin, Md Mashrur, Based, Md Mashrur, Mumenin, Khondoker Mirazul, Imran, Ali, Azim, Mohammad Abdul, Alom, Zulfikar and Awal, Md Abdul. 2022. "OLGBM: Optuna optimized light gradient boosting machine for intrusion detection." 2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2). Rajshahi, Bangladesh 26 - 27 Dec 2021 Bangladesh. IEEE (Institute of Electrical and Electronics Engineers). https://doi.org/10.1109/IC4ME253898.2021.9768555
Paper/Presentation Title

OLGBM: Optuna optimized light gradient boosting machine for intrusion detection

Presentation TypePaper
AuthorsArifin, Md Mashrur, Based, Md Mashrur, Mumenin, Khondoker Mirazul, Imran, Ali, Azim, Mohammad Abdul, Alom, Zulfikar and Awal, Md Abdul
Journal or Proceedings TitleProceedings of 2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)
Number of Pages4
Year2022
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Place of PublicationBangladesh
ISBN9781665406376
9781665406383
Digital Object Identifier (DOI)https://doi.org/10.1109/IC4ME253898.2021.9768555
Web Address (URL) of Paperhttps://ieeexplore.ieee.org/document/9768555
Web Address (URL) of Conference Proceedingshttps://ieeexplore.ieee.org/xpl/conhome/9768399/proceeding
Conference/Event2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)
Event Details
2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2)
Delivery
In person
Event Date
26 to end of 27 Dec 2021
Event Location
Rajshahi, Bangladesh
Abstract

Network technology has been evolved exponentially in the past few decades. At the same time, gazillions of network intrusion incidents are continuously forming cyberspace a shocking vulnerable domain to explore for the personnel from armature to the professionals. Consequently, networks mostly become botnet when there are no Intrusion Detection Systems (IDS) deployed. A smart and efficient intrusion detection system is an inexorable solution designed for fine-tuning the preventive rule-sets in any network. In this paper, we have proposed an efficient anomaly-based IDS mechanism. This detection mechanism has been competent by three tree-based state-of-the-art machine learning classifiers, namely, Random Forest (RF), Decision Tree (DT), and Optuna based Light Gradient Boosting Machine (OLGBM) algorithms. A comparative study of the algorithmic performances has been executed to determine the best algorithm that could be efficient for the IDS. In the experiment, the proposed OLGBM model gives better performance (accuracy 98.46%).

KeywordsAnomaly detection; Feature selection; Optuna; LGBM; Machine learning; Intrusion detection
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 2020461199. Machine learning not elsewhere classified
Public Notes

Files associated with this item cannot be displayed due to copyright restrictions.

Byline AffiliationsDhaka International University, Bangladesh
Khulna University, Bangladesh
Asian University for Women, Bangladesh
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